In [706]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
import pandas_datareader as web

pd.options.display.max_rows = None

Energy Stocks

In [2]:
#S&P 500 Benchmark
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
sp = web.DataReader('^GSPC', 'yahoo', start, end)
sp['returns'] = sp['Close'].pct_change(1)
sp['Cumulative Return'] = (1 + sp['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
spmean = sp['returns'].mean()
spvar = sp['returns'].var()
spkurt = sp['returns'].kurt()
spskew = sp['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
spbef = web.DataReader('^GSPC', 'yahoo', start, crashstart)
spbef['avgreturnbefore'] = spbef['Close'].pct_change(1)
spbef['Cumulative Return Before Crash'] = (1 + spbef['avgreturnbefore']).cumprod()

#period of crash
spdur = web.DataReader('^GSPC', 'yahoo', crashstart, crashend)
spdur['avgreturnduring'] = spdur['Close'].pct_change(1)
spdur['Cumulative Return During Crash'] = (1 + spdur['avgreturnduring']).cumprod()

#period after crash
spaft = web.DataReader('^GSPC', 'yahoo', crashend, end)
spaft['avgreturnafter'] = spaft['Close'].pct_change(1)
spaft['Cumulative Return After Crash'] = (1 + spaft['avgreturnafter']).cumprod()
In [3]:
#Energy Stocks

start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
apache = web.DataReader('APA', 'yahoo', start, end)
apache['returns'] = apache['Close'].pct_change(1)
apache['Cumulative Return'] = (1 + apache['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
apachemean = apache['returns'].mean()
apachevar = apache['returns'].var()
apachekurt = apache['returns'].kurt()
apacheskew = apache['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
apachebef = web.DataReader('APA', 'yahoo', start, crashstart)
apachebef['avgreturnbefore'] = apachebef['Close'].pct_change(1)
apachebef['Cumulative Return Before Crash'] = (1 + apachebef['avgreturnbefore']).cumprod()

#period of crash
apachedur = web.DataReader('APA', 'yahoo', crashstart, crashend)
apachedur['avgreturnduring'] = apachedur['Close'].pct_change(1)
apachedur['Cumulative Return During Crash'] = (1 + apachedur['avgreturnduring']).cumprod()

#period after crash
apacheaft = web.DataReader('APA', 'yahoo', crashend, end)
apacheaft['avgreturnafter'] = apacheaft['Close'].pct_change(1)
apacheaft['Cumulative Return After Crash'] = (1 + apacheaft['avgreturnafter']).cumprod()
In [4]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
baker = web.DataReader('BKR', 'yahoo', start, end)
baker['returns'] = baker['Close'].pct_change(1)
baker['Cumulative Return'] = (1 + baker['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
bakermean = baker['returns'].mean()
bakervar = baker['returns'].var()
bakerkurt = baker['returns'].kurt()
bakerskew = baker['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
bakerbef = web.DataReader('BKR', 'yahoo', start, crashstart)
bakerbef['avgreturnbefore'] = bakerbef['Close'].pct_change(1)
bakerbef['Cumulative Return Before Crash'] = (1 + bakerbef['avgreturnbefore']).cumprod()

#period of crash
bakerdur = web.DataReader('BKR', 'yahoo', crashstart, crashend)
bakerdur['avgreturnduring'] = bakerdur['Close'].pct_change(1)
bakerdur['Cumulative Return During Crash'] = (1 + bakerdur['avgreturnduring']).cumprod()

#period after crash
bakeraft = web.DataReader('BKR', 'yahoo', crashend, end)
bakeraft['avgreturnafter'] = bakeraft['Close'].pct_change(1)
bakeraft['Cumulative Return After Crash'] = (1 + bakeraft['avgreturnafter']).cumprod()
In [5]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
cabot = web.DataReader('COG', 'yahoo', start, end)
cabot['returns'] = cabot['Close'].pct_change(1)
cabot['Cumulative Return'] = (1 + cabot['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
cabotmean = cabot['returns'].mean()
cabotvar = cabot['returns'].var()
cabotkurt = cabot['returns'].kurt()
cabotskew = cabot['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
cabotbef = web.DataReader('COG', 'yahoo', start, crashstart)
cabotbef['avgreturnbefore'] = cabotbef['Close'].pct_change(1)
cabotbef['Cumulative Return Before Crash'] = (1 + cabotbef['avgreturnbefore']).cumprod()

#period of crash
cabotdur = web.DataReader('COG', 'yahoo', crashstart, crashend)
cabotdur['avgreturnduring'] = cabotdur['Close'].pct_change(1)
cabotdur['Cumulative Return During Crash'] = (1 + cabotdur['avgreturnduring']).cumprod()

#period after crash
cabotaft = web.DataReader('COG', 'yahoo', crashend, end)
cabotaft['avgreturnafter'] = cabotaft['Close'].pct_change(1)
cabotaft['Cumulative Return After Crash'] = (1 + cabotaft['avgreturnafter']).cumprod()
In [6]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
chevron = web.DataReader('CVX', 'yahoo', start, end)
chevron['returns'] = chevron['Close'].pct_change(1)
chevron['Cumulative Return'] = (1 + chevron['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
chevronmean = chevron['returns'].mean()
chevronvar = chevron['returns'].var()
chevronkurt = chevron['returns'].kurt()
chevronskew = chevron['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
chevronbef = web.DataReader('CVX', 'yahoo', start, crashstart)
chevronbef['avgreturnbefore'] = chevronbef['Close'].pct_change(1)
chevronbef['Cumulative Return Before Crash'] = (1 + chevronbef['avgreturnbefore']).cumprod()

#period of crash
chevrondur = web.DataReader('CVX', 'yahoo', crashstart, crashend)
chevrondur['avgreturnduring'] = chevrondur['Close'].pct_change(1)
chevrondur['Cumulative Return During Crash'] = (1 + chevrondur['avgreturnduring']).cumprod()

#period after crash
chevronaft = web.DataReader('CVX', 'yahoo', crashend, end)
chevronaft['avgreturnafter'] = chevronaft['Close'].pct_change(1)
chevronaft['Cumulative Return After Crash'] = (1 + chevronaft['avgreturnafter']).cumprod()
In [7]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
concho = web.DataReader('CXO', 'yahoo', start, end)
concho['returns'] = concho['Close'].pct_change(1)
concho['Cumulative Return'] = (1 + concho['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
conchomean = concho['returns'].mean()
conchovar = concho['returns'].var()
conchokurt = concho['returns'].kurt()
conchoskew = concho['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
conchobef = web.DataReader('CXO', 'yahoo', start, crashstart)
conchobef['avgreturnbefore'] = conchobef['Close'].pct_change(1)
conchobef['Cumulative Return Before Crash'] = (1 + conchobef['avgreturnbefore']).cumprod()

#period of crash
conchodur = web.DataReader('CXO', 'yahoo', crashstart, crashend)
conchodur['avgreturnduring'] = conchodur['Close'].pct_change(1)
conchodur['Cumulative Return During Crash'] = (1 + conchodur['avgreturnduring']).cumprod()

#period after crash
conchoaft = web.DataReader('CXO', 'yahoo', crashend, end)
conchoaft['avgreturnafter'] = conchoaft['Close'].pct_change(1)
conchoaft['Cumulative Return After Crash'] = (1 + conchoaft['avgreturnafter']).cumprod()
In [8]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
conoco = web.DataReader('COP', 'yahoo', start, end)
conoco['returns'] = conoco['Close'].pct_change(1)
conoco['Cumulative Return'] = (1 + conoco['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
conocomean = conoco['returns'].mean()
conocovar = conoco['returns'].var()
conocokurt = conoco['returns'].kurt()
conocoskew = conoco['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
conocobef = web.DataReader('COP', 'yahoo', start, crashstart)
conocobef['avgreturnbefore'] = conocobef['Close'].pct_change(1)
conocobef['Cumulative Return Before Crash'] = (1 + conocobef['avgreturnbefore']).cumprod()

#period of crash
conocodur = web.DataReader('COP', 'yahoo', crashstart, crashend)
conocodur['avgreturnduring'] = conocodur['Close'].pct_change(1)
conocodur['Cumulative Return During Crash'] = (1 + conocodur['avgreturnduring']).cumprod()

#period after crash
conocoaft = web.DataReader('COP', 'yahoo', crashend, end)
conocoaft['avgreturnafter'] = conocoaft['Close'].pct_change(1)
conocoaft['Cumulative Return After Crash'] = (1 + conocoaft['avgreturnafter']).cumprod()
In [9]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
devon = web.DataReader('DVN', 'yahoo', start, end)
devon['returns'] = devon['Close'].pct_change(1)
devon['Cumulative Return'] = (1 + devon['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
devonmean = devon['returns'].mean()
devonvar = devon['returns'].var()
devonkurt = devon['returns'].kurt()
devonskew = devon['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
devonbef = web.DataReader('DVN', 'yahoo', start, crashstart)
devonbef['avgreturnbefore'] = devonbef['Close'].pct_change(1)
devonbef['Cumulative Return Before Crash'] = (1 + devonbef['avgreturnbefore']).cumprod()

#period of crash
devondur = web.DataReader('DVN', 'yahoo', crashstart, crashend)
devondur['avgreturnduring'] = devondur['Close'].pct_change(1)
devondur['Cumulative Return During Crash'] = (1 + devondur['avgreturnduring']).cumprod()

#period after crash
devonaft = web.DataReader('DVN', 'yahoo', crashend, end)
devonaft['avgreturnafter'] = devonaft['Close'].pct_change(1)
devonaft['Cumulative Return After Crash'] = (1 + devonaft['avgreturnafter']).cumprod()
In [10]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
diamond = web.DataReader('FANG', 'yahoo', start, end)
diamond['returns'] = diamond['Close'].pct_change(1)
diamond['Cumulative Return'] = (1 + diamond['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
diamondmean = diamond['returns'].mean()
diamondvar = diamond['returns'].var()
diamondkurt = diamond['returns'].kurt()
diamondskew = diamond['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
diamondbef = web.DataReader('FANG', 'yahoo', start, crashstart)
diamondbef['avgreturnbefore'] = diamondbef['Close'].pct_change(1)
diamondbef['Cumulative Return Before Crash'] = (1 + diamondbef['avgreturnbefore']).cumprod()

#period of crash
diamonddur = web.DataReader('FANG', 'yahoo', crashstart, crashend)
diamonddur['avgreturnduring'] = diamonddur['Close'].pct_change(1)
diamonddur['Cumulative Return During Crash'] = (1 + diamonddur['avgreturnduring']).cumprod()

#period after crash
diamondaft = web.DataReader('FANG', 'yahoo', crashend, end)
diamondaft['avgreturnafter'] = diamondaft['Close'].pct_change(1)
diamondaft['Cumulative Return After Crash'] = (1 + diamondaft['avgreturnafter']).cumprod()
In [11]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EOG = web.DataReader('EOG', 'yahoo', start, end)
EOG['returns'] = EOG['Close'].pct_change(1)
EOG['Cumulative Return'] = (1 + EOG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EOGmean = EOG['returns'].mean()
EOGvar = EOG['returns'].var()
EOGkurt = EOG['returns'].kurt()
EOGskew = EOG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EOGbef = web.DataReader('EOG', 'yahoo', start, crashstart)
EOGbef['avgreturnbefore'] = EOGbef['Close'].pct_change(1)
EOGbef['Cumulative Return Before Crash'] = (1 + EOGbef['avgreturnbefore']).cumprod()

#period of crash
EOGdur = web.DataReader('EOG', 'yahoo', crashstart, crashend)
EOGdur['avgreturnduring'] = EOGdur['Close'].pct_change(1)
EOGdur['Cumulative Return During Crash'] = (1 + EOGdur['avgreturnduring']).cumprod()

#period after crash
EOGaft = web.DataReader('EOG', 'yahoo', crashend, end)
EOGaft['avgreturnafter'] = EOGaft['Close'].pct_change(1)
EOGaft['Cumulative Return After Crash'] = (1 + EOGaft['avgreturnafter']).cumprod()
In [12]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
exxon = web.DataReader('XOM', 'yahoo', start, end)
exxon['returns'] = exxon['Close'].pct_change(1)
exxon['Cumulative Return'] = (1 + exxon['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
exxonmean = exxon['returns'].mean()
exxonvar = exxon['returns'].var()
exxonkurt = exxon['returns'].kurt()
exxonskew = exxon['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
exxonbef = web.DataReader('XOM', 'yahoo', start, crashstart)
exxonbef['avgreturnbefore'] = exxonbef['Close'].pct_change(1)
exxonbef['Cumulative Return Before Crash'] = (1 + exxonbef['avgreturnbefore']).cumprod()

#period of crash
exxondur = web.DataReader('XOM', 'yahoo', crashstart, crashend)
exxondur['avgreturnduring'] = exxondur['Close'].pct_change(1)
exxondur['Cumulative Return During Crash'] = (1 + exxondur['avgreturnduring']).cumprod()

#period after crash
exxonaft = web.DataReader('XOM', 'yahoo', crashend, end)
exxonaft['avgreturnafter'] = exxonaft['Close'].pct_change(1)
exxonaft['Cumulative Return After Crash'] = (1 + exxonaft['avgreturnafter']).cumprod()
In [13]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
halliburton = web.DataReader('HAL', 'yahoo', start, end)
halliburton['returns'] = halliburton['Close'].pct_change(1)
halliburton['Cumulative Return'] = (1 + halliburton['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
halliburtonmean = halliburton['returns'].mean()
halliburtonvar = halliburton['returns'].var()
halliburtonkurt = halliburton['returns'].kurt()
halliburtonskew = halliburton['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
halliburtonbef = web.DataReader('HAL', 'yahoo', start, crashstart)
halliburtonbef['avgreturnbefore'] = halliburtonbef['Close'].pct_change(1)
halliburtonbef['Cumulative Return Before Crash'] = (1 + halliburtonbef['avgreturnbefore']).cumprod()

#period of crash
halliburtondur = web.DataReader('HAL', 'yahoo', crashstart, crashend)
halliburtondur['avgreturnduring'] = halliburtondur['Close'].pct_change(1)
halliburtondur['Cumulative Return During Crash'] = (1 + halliburtondur['avgreturnduring']).cumprod()

#period after crash
halliburtonaft = web.DataReader('HAL', 'yahoo', crashend, end)
halliburtonaft['avgreturnafter'] = halliburtonaft['Close'].pct_change(1)
halliburtonaft['Cumulative Return After Crash'] = (1 + halliburtonaft['avgreturnafter']).cumprod()
In [14]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
hess = web.DataReader('HES', 'yahoo', start, end)
hess['returns'] = hess['Close'].pct_change(1)
hess['Cumulative Return'] = (1 + hess['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
hessmean = hess['returns'].mean()
hessvar = hess['returns'].var()
hesskurt = hess['returns'].kurt()
hessskew = hess['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
hessbef = web.DataReader('HES', 'yahoo', start, crashstart)
hessbef['avgreturnbefore'] = hessbef['Close'].pct_change(1)
hessbef['Cumulative Return Before Crash'] = (1 + hessbef['avgreturnbefore']).cumprod()

#period of crash
hessdur = web.DataReader('HES', 'yahoo', crashstart, crashend)
hessdur['avgreturnduring'] = hessdur['Close'].pct_change(1)
hessdur['Cumulative Return During Crash'] = (1 + hessdur['avgreturnduring']).cumprod()

#period after crash
hessaft = web.DataReader('HES', 'yahoo', crashend, end)
hessaft['avgreturnafter'] = hessaft['Close'].pct_change(1)
hessaft['Cumulative Return After Crash'] = (1 + hessaft['avgreturnafter']).cumprod()
In [15]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
holly = web.DataReader('HFC', 'yahoo', start, end)
holly['returns'] = holly['Close'].pct_change(1)
holly['Cumulative Return'] = (1 + holly['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
hollymean = holly['returns'].mean()
hollyvar = holly['returns'].var()
hollykurt = holly['returns'].kurt()
hollyskew = holly['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
hollybef = web.DataReader('HFC', 'yahoo', start, crashstart)
hollybef['avgreturnbefore'] = hollybef['Close'].pct_change(1)
hollybef['Cumulative Return Before Crash'] = (1 + hollybef['avgreturnbefore']).cumprod()

#period of crash
hollydur = web.DataReader('HFC', 'yahoo', crashstart, crashend)
hollydur['avgreturnduring'] = hollydur['Close'].pct_change(1)
hollydur['Cumulative Return During Crash'] = (1 + hollydur['avgreturnduring']).cumprod()

#period after crash
hollyaft = web.DataReader('HFC', 'yahoo', crashend, end)
hollyaft['avgreturnafter'] = hollyaft['Close'].pct_change(1)
hollyaft['Cumulative Return After Crash'] = (1 + hollyaft['avgreturnafter']).cumprod()
In [16]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
kinder = web.DataReader('KMI', 'yahoo', start, end)
kinder['returns'] = kinder['Close'].pct_change(1)
kinder['Cumulative Return'] = (1 + kinder['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
kindermean = kinder['returns'].mean()
kindervar = kinder['returns'].var()
kinderkurt = kinder['returns'].kurt()
kinderskew = kinder['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
kinderbef = web.DataReader('KMI', 'yahoo', start, crashstart)
kinderbef['avgreturnbefore'] = kinderbef['Close'].pct_change(1)
kinderbef['Cumulative Return Before Crash'] = (1 + kinderbef['avgreturnbefore']).cumprod()

#period of crash
kinderdur = web.DataReader('KMI', 'yahoo', crashstart, crashend)
kinderdur['avgreturnduring'] = kinderdur['Close'].pct_change(1)
kinderdur['Cumulative Return During Crash'] = (1 + kinderdur['avgreturnduring']).cumprod()

#period after crash
kinderaft = web.DataReader('KMI', 'yahoo', crashend, end)
kinderaft['avgreturnafter'] = kinderaft['Close'].pct_change(1)
kinderaft['Cumulative Return After Crash'] = (1 + kinderaft['avgreturnafter']).cumprod()
In [17]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
marathonoil = web.DataReader('MRO', 'yahoo', start, end)
marathonoil['returns'] = marathonoil['Close'].pct_change(1)
marathonoil['Cumulative Return'] = (1 + marathonoil['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
marathonoilmean = marathonoil['returns'].mean()
marathonoilvar = marathonoil['returns'].var()
marathonoilkurt = marathonoil['returns'].kurt()
marathonoilskew = marathonoil['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
marathonoilbef = web.DataReader('MRO', 'yahoo', start, crashstart)
marathonoilbef['avgreturnbefore'] = marathonoilbef['Close'].pct_change(1)
marathonoilbef['Cumulative Return Before Crash'] = (1 + marathonoilbef['avgreturnbefore']).cumprod()

#period of crash
marathonoildur = web.DataReader('MRO', 'yahoo', crashstart, crashend)
marathonoildur['avgreturnduring'] = marathonoildur['Close'].pct_change(1)
marathonoildur['Cumulative Return During Crash'] = (1 + marathonoildur['avgreturnduring']).cumprod()

#period after crash
marathonoilaft = web.DataReader('MRO', 'yahoo', crashend, end)
marathonoilaft['avgreturnafter'] = marathonoilaft['Close'].pct_change(1)
marathonoilaft['Cumulative Return After Crash'] = (1 + marathonoilaft['avgreturnafter']).cumprod()
In [18]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
marathonpet = web.DataReader('MPC', 'yahoo', start, end)
marathonpet['returns'] = marathonpet['Close'].pct_change(1)
marathonpet['Cumulative Return'] = (1 + marathonpet['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
marathonpetmean = marathonpet['returns'].mean()
marathonpetvar = marathonpet['returns'].var()
marathonpetkurt = marathonpet['returns'].kurt()
marathonpetskew = marathonpet['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
marathonpetbef = web.DataReader('MPC', 'yahoo', start, crashstart)
marathonpetbef['avgreturnbefore'] = marathonpetbef['Close'].pct_change(1)
marathonpetbef['Cumulative Return Before Crash'] = (1 + marathonpetbef['avgreturnbefore']).cumprod()

#period of crash
marathonpetdur = web.DataReader('MPC', 'yahoo', crashstart, crashend)
marathonpetdur['avgreturnduring'] = marathonpetdur['Close'].pct_change(1)
marathonpetdur['Cumulative Return During Crash'] = (1 + marathonpetdur['avgreturnduring']).cumprod()

#period after crash
marathonpetaft = web.DataReader('MPC', 'yahoo', crashend, end)
marathonpetaft['avgreturnafter'] = marathonpetaft['Close'].pct_change(1)
marathonpetaft['Cumulative Return After Crash'] = (1 + marathonpetaft['avgreturnafter']).cumprod()
In [19]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
national = web.DataReader('NOV', 'yahoo', start, end)
national['returns'] = national['Close'].pct_change(1)
national['Cumulative Return'] = (1 + national['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
nationalmean = national['returns'].mean()
nationalvar = national['returns'].var()
nationalkurt = national['returns'].kurt()
nationalskew = national['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
nationalbef = web.DataReader('NOV', 'yahoo', start, crashstart)
nationalbef['avgreturnbefore'] = nationalbef['Close'].pct_change(1)
nationalbef['Cumulative Return Before Crash'] = (1 + nationalbef['avgreturnbefore']).cumprod()

#period of crash
nationaldur = web.DataReader('NOV', 'yahoo', crashstart, crashend)
nationaldur['avgreturnduring'] = nationaldur['Close'].pct_change(1)
nationaldur['Cumulative Return During Crash'] = (1 + nationaldur['avgreturnduring']).cumprod()

#period after crash
nationalaft = web.DataReader('NOV', 'yahoo', crashend, end)
nationalaft['avgreturnafter'] = nationalaft['Close'].pct_change(1)
nationalaft['Cumulative Return After Crash'] = (1 + nationalaft['avgreturnafter']).cumprod()
In [20]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
noble = web.DataReader('NBL', 'yahoo', start, end)
noble['returns'] = noble['Close'].pct_change(1)
noble['Cumulative Return'] = (1 + noble['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
noblemean = noble['returns'].mean()
noblevar = noble['returns'].var()
noblekurt = noble['returns'].kurt()
nobleskew = noble['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
noblebef = web.DataReader('NBL', 'yahoo', start, crashstart)
noblebef['avgreturnbefore'] = noblebef['Close'].pct_change(1)
noblebef['Cumulative Return Before Crash'] = (1 + noblebef['avgreturnbefore']).cumprod()

#period of crash
nobledur = web.DataReader('NBL', 'yahoo', crashstart, crashend)
nobledur['avgreturnduring'] = nobledur['Close'].pct_change(1)
nobledur['Cumulative Return During Crash'] = (1 + nobledur['avgreturnduring']).cumprod()

#period after crash
nobleaft = web.DataReader('NBL', 'yahoo', crashend, end)
nobleaft['avgreturnafter'] = nobleaft['Close'].pct_change(1)
nobleaft['Cumulative Return After Crash'] = (1 + nobleaft['avgreturnafter']).cumprod()
In [21]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
occidental = web.DataReader('OXY', 'yahoo', start, end)
occidental['returns'] = occidental['Close'].pct_change(1)
occidental['Cumulative Return'] = (1 + occidental['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
occidentalmean = occidental['returns'].mean()
occidentalvar = occidental['returns'].var()
occidentalkurt = occidental['returns'].kurt()
occidentalskew = occidental['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
occidentalbef = web.DataReader('OXY', 'yahoo', start, crashstart)
occidentalbef['avgreturnbefore'] = occidentalbef['Close'].pct_change(1)
occidentalbef['Cumulative Return Before Crash'] = (1 + occidentalbef['avgreturnbefore']).cumprod()

#period of crash
occidentaldur = web.DataReader('OXY', 'yahoo', crashstart, crashend)
occidentaldur['avgreturnduring'] = occidentaldur['Close'].pct_change(1)
occidentaldur['Cumulative Return During Crash'] = (1 + occidentaldur['avgreturnduring']).cumprod()

#period after crash
occidentalaft = web.DataReader('OXY', 'yahoo', crashend, end)
occidentalaft['avgreturnafter'] = occidentalaft['Close'].pct_change(1)
occidentalaft['Cumulative Return After Crash'] = (1 + occidentalaft['avgreturnafter']).cumprod()
In [22]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
oneok = web.DataReader('OKE', 'yahoo', start, end)
oneok['returns'] = oneok['Close'].pct_change(1)
oneok['Cumulative Return'] = (1 + oneok['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
oneokmean = oneok['returns'].mean()
oneokvar = oneok['returns'].var()
oneokkurt = oneok['returns'].kurt()
oneokskew = oneok['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
oneokbef = web.DataReader('OKE', 'yahoo', start, crashstart)
oneokbef['avgreturnbefore'] = oneokbef['Close'].pct_change(1)
oneokbef['Cumulative Return Before Crash'] = (1 + oneokbef['avgreturnbefore']).cumprod()

#period of crash
oneokdur = web.DataReader('OKE', 'yahoo', crashstart, crashend)
oneokdur['avgreturnduring'] = oneokdur['Close'].pct_change(1)
oneokdur['Cumulative Return During Crash'] = (1 + oneokdur['avgreturnduring']).cumprod()

#period after crash
oneokaft = web.DataReader('OKE', 'yahoo', crashend, end)
oneokaft['avgreturnafter'] = oneokaft['Close'].pct_change(1)
oneokaft['Cumulative Return After Crash'] = (1 + oneokaft['avgreturnafter']).cumprod()
In [23]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
phillips66 = web.DataReader('PSX', 'yahoo', start, end)
phillips66['returns'] = phillips66['Close'].pct_change(1)
phillips66['Cumulative Return'] = (1 + phillips66['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
phillips66mean = phillips66['returns'].mean()
phillips66var = phillips66['returns'].var()
phillips66kurt = phillips66['returns'].kurt()
phillips66skew = phillips66['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
phillips66bef = web.DataReader('PSX', 'yahoo', start, crashstart)
phillips66bef['avgreturnbefore'] = phillips66bef['Close'].pct_change(1)
phillips66bef['Cumulative Return Before Crash'] = (1 + phillips66bef['avgreturnbefore']).cumprod()

#period of crash
phillips66dur = web.DataReader('PSX', 'yahoo', crashstart, crashend)
phillips66dur['avgreturnduring'] = phillips66dur['Close'].pct_change(1)
phillips66dur['Cumulative Return During Crash'] = (1 + phillips66dur['avgreturnduring']).cumprod()

#period after crash
phillips66aft = web.DataReader('PSX', 'yahoo', crashend, end)
phillips66aft['avgreturnafter'] = phillips66aft['Close'].pct_change(1)
phillips66aft['Cumulative Return After Crash'] = (1 + phillips66aft['avgreturnafter']).cumprod()
In [24]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
pioneer = web.DataReader('PXD', 'yahoo', start, end)
pioneer['returns'] = pioneer['Close'].pct_change(1)
pioneer['Cumulative Return'] = (1 + pioneer['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
pioneermean = pioneer['returns'].mean()
pioneervar = pioneer['returns'].var()
pioneerkurt = pioneer['returns'].kurt()
pioneerskew = pioneer['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
pioneerbef = web.DataReader('PXD', 'yahoo', start, crashstart)
pioneerbef['avgreturnbefore'] = pioneerbef['Close'].pct_change(1)
pioneerbef['Cumulative Return Before Crash'] = (1 + pioneerbef['avgreturnbefore']).cumprod()

#period of crash
pioneerdur = web.DataReader('PXD', 'yahoo', crashstart, crashend)
pioneerdur['avgreturnduring'] = pioneerdur['Close'].pct_change(1)
pioneerdur['Cumulative Return During Crash'] = (1 + pioneerdur['avgreturnduring']).cumprod()

#period after crash
pioneeraft = web.DataReader('PXD', 'yahoo', crashend, end)
pioneeraft['avgreturnafter'] = pioneeraft['Close'].pct_change(1)
pioneeraft['Cumulative Return After Crash'] = (1 + pioneeraft['avgreturnafter']).cumprod()
In [25]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
schlumberger = web.DataReader('SLB', 'yahoo', start, end)
schlumberger['returns'] = schlumberger['Close'].pct_change(1)
schlumberger['Cumulative Return'] = (1 + schlumberger['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
schlumbergermean = schlumberger['returns'].mean()
schlumbergervar = schlumberger['returns'].var()
schlumbergerkurt = schlumberger['returns'].kurt()
schlumbergerskew = schlumberger['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
schlumbergerbef = web.DataReader('SLB', 'yahoo', start, crashstart)
schlumbergerbef['avgreturnbefore'] = schlumbergerbef['Close'].pct_change(1)
schlumbergerbef['Cumulative Return Before Crash'] = (1 + schlumbergerbef['avgreturnbefore']).cumprod()

#period of crash
schlumbergerdur = web.DataReader('SLB', 'yahoo', crashstart, crashend)
schlumbergerdur['avgreturnduring'] = schlumbergerdur['Close'].pct_change(1)
schlumbergerdur['Cumulative Return During Crash'] = (1 + schlumbergerdur['avgreturnduring']).cumprod()

#period after crash
schlumbergeraft = web.DataReader('SLB', 'yahoo', crashend, end)
schlumbergeraft['avgreturnafter'] = schlumbergeraft['Close'].pct_change(1)
schlumbergeraft['Cumulative Return After Crash'] = (1 + schlumbergeraft['avgreturnafter']).cumprod()
In [26]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
technip = web.DataReader('FTI', 'yahoo', start, end)
technip['returns'] = technip['Close'].pct_change(1)
technip['Cumulative Return'] = (1 + technip['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
technipmean = technip['returns'].mean()
technipvar = technip['returns'].var()
technipkurt = technip['returns'].kurt()
technipskew = technip['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
technipbef = web.DataReader('FTI', 'yahoo', start, crashstart)
technipbef['avgreturnbefore'] = technipbef['Close'].pct_change(1)
technipbef['Cumulative Return Before Crash'] = (1 + technipbef['avgreturnbefore']).cumprod()

#period of crash
technipdur = web.DataReader('FTI', 'yahoo', crashstart, crashend)
technipdur['avgreturnduring'] = technipdur['Close'].pct_change(1)
technipdur['Cumulative Return During Crash'] = (1 + technipdur['avgreturnduring']).cumprod()

#period after crash
technipaft = web.DataReader('FTI', 'yahoo', crashend, end)
technipaft['avgreturnafter'] = technipaft['Close'].pct_change(1)
technipaft['Cumulative Return After Crash'] = (1 + technipaft['avgreturnafter']).cumprod()
In [27]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
valero = web.DataReader('VLO', 'yahoo', start, end)
valero['returns'] = valero['Close'].pct_change(1)
valero['Cumulative Return'] = (1 + valero['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
valeromean = valero['returns'].mean()
valerovar = valero['returns'].var()
valerokurt = valero['returns'].kurt()
valeroskew = valero['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
valerobef = web.DataReader('VLO', 'yahoo', start, crashstart)
valerobef['avgreturnbefore'] = valerobef['Close'].pct_change(1)
valerobef['Cumulative Return Before Crash'] = (1 + valerobef['avgreturnbefore']).cumprod()

#period of crash
valerodur = web.DataReader('VLO', 'yahoo', crashstart, crashend)
valerodur['avgreturnduring'] = valerodur['Close'].pct_change(1)
valerodur['Cumulative Return During Crash'] = (1 + valerodur['avgreturnduring']).cumprod()

#period after crash
valeroaft = web.DataReader('VLO', 'yahoo', crashend, end)
valeroaft['avgreturnafter'] = valeroaft['Close'].pct_change(1)
valeroaft['Cumulative Return After Crash'] = (1 + valeroaft['avgreturnafter']).cumprod()
In [28]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
williams = web.DataReader('WMB', 'yahoo', start, end)
williams['returns'] = williams['Close'].pct_change(1)
williams['Cumulative Return'] = (1 + williams['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
williamsmean = williams['returns'].mean()
williamsvar = williams['returns'].var()
williamskurt = williams['returns'].kurt()
williamsskew = williams['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
williamsbef = web.DataReader('WMB', 'yahoo', start, crashstart)
williamsbef['avgreturnbefore'] = williamsbef['Close'].pct_change(1)
williamsbef['Cumulative Return Before Crash'] = (1 + williamsbef['avgreturnbefore']).cumprod()

#period of crash
williamsdur = web.DataReader('WMB', 'yahoo', crashstart, crashend)
williamsdur['avgreturnduring'] = williamsdur['Close'].pct_change(1)
williamsdur['Cumulative Return During Crash'] = (1 + williamsdur['avgreturnduring']).cumprod()

#period after crash
williamsaft = web.DataReader('WMB', 'yahoo', crashend, end)
williamsaft['avgreturnafter'] = williamsaft['Close'].pct_change(1)
williamsaft['Cumulative Return After Crash'] = (1 + williamsaft['avgreturnafter']).cumprod()
In [29]:
#histogram of Energy Stock Returns
sp['returns'].hist(bins = 50, label ='S&P 500', alpha=0.5)
apache['returns'].hist(bins = 50, label ='Apache', figsize = (10,8), alpha=0.5)
baker['returns'].hist(bins = 50, label ='Baker', alpha=0.5)
cabot['returns'].hist(bins = 50, label ='Cabot', alpha=0.5)
chevron['returns'].hist(bins = 50, label ='Chevron', alpha=0.5)
concho['returns'].hist(bins = 50, label ='Concho', alpha=0.5)
conoco['returns'].hist(bins = 50, label ='Conoco', alpha=0.5)
devon['returns'].hist(bins = 50, label ='Devon', alpha=0.5)
diamond['returns'].hist(bins = 50, label ='Diamond', alpha=0.5)
EOG['returns'].hist(bins = 50, label ='EOG', alpha=0.5)
exxon['returns'].hist(bins = 50, label ='Exxon', alpha=0.5)
halliburton['returns'].hist(bins = 50, label ='Halliburton', alpha=0.5)
hess['returns'].hist(bins = 50, label ='Hess', alpha=0.5)
holly['returns'].hist(bins = 50, label ='Holly', alpha=0.5)
kinder['returns'].hist(bins = 50, label ='Kinder', alpha=0.5)
marathonoil['returns'].hist(bins = 50, label ='MarathonOil', alpha=0.5)
marathonpet['returns'].hist(bins = 50, label ='MarathonPet', alpha=0.5)
national['returns'].hist(bins = 50, label ='National', alpha=0.5)
noble['returns'].hist(bins = 50, label ='Noble', alpha=0.5)
occidental['returns'].hist(bins = 50, label ='Occidental', alpha=0.5)
oneok['returns'].hist(bins = 50, label ='Oneok', alpha=0.5)
phillips66['returns'].hist(bins = 50, label ='Phillips66', alpha=0.5)
pioneer['returns'].hist(bins = 50, label ='Pioneer', alpha=0.5)
schlumberger['returns'].hist(bins = 50, label ='Schlumberger', alpha=0.5)
technip['returns'].hist(bins = 50, label ='Technip', alpha=0.5)
valero['returns'].hist(bins = 50, label ='Valero', alpha=0.5)
williams['returns'].hist(bins = 50, label ='Williams', alpha=0.5)
plt.legend()
Out[29]:
<matplotlib.legend.Legend at 0x13f9a4abf98>
In [30]:
#KDE Plot for Energy Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
apache['returns'].plot(kind = 'kde', label ='Apache', figsize = (10,8), alpha=0.5, title = 'KDE of Energy Stocks')
baker['returns'].plot(kind = 'kde', label ='Baker', alpha=0.5)
cabot['returns'].plot(kind = 'kde', label ='Cabot', alpha=0.5)
chevron['returns'].plot(kind = 'kde', label ='Chevron', alpha=0.5)
concho['returns'].plot(kind = 'kde', label ='Concho', alpha=0.5)
conoco['returns'].plot(kind = 'kde', label ='Conoco', alpha=0.5)
devon['returns'].plot(kind = 'kde', label ='Devon', alpha=0.5)
diamond['returns'].plot(kind = 'kde', label ='Diamond', alpha=0.5)
EOG['returns'].plot(kind = 'kde', label ='EOG', alpha=0.5)
exxon['returns'].plot(kind = 'kde', label ='Exxon', alpha=0.5)
halliburton['returns'].plot(kind = 'kde', label ='Halliburton', alpha=0.5)
hess['returns'].plot(kind = 'kde', label ='Hess', alpha=0.5)
holly['returns'].plot(kind = 'kde', label ='Holly', alpha=0.5)
kinder['returns'].plot(kind = 'kde', label ='Kinder', alpha=0.5)
marathonoil['returns'].plot(kind = 'kde', label ='MarathonOil', alpha=0.5)
marathonpet['returns'].plot(kind = 'kde', label ='MarathonPet', alpha=0.5)
national['returns'].plot(kind = 'kde', label ='National', alpha=0.5)
noble['returns'].plot(kind = 'kde', label ='Noble', alpha=0.5)
occidental['returns'].plot(kind = 'kde', label ='Occidental', alpha=0.5)
oneok['returns'].plot(kind = 'kde', label ='Oneok', alpha=0.5)
phillips66['returns'].plot(kind = 'kde', label ='Phillips66', alpha=0.5)
pioneer['returns'].plot(kind = 'kde', label ='Pioneer', alpha=0.5)
schlumberger['returns'].plot(kind = 'kde', label ='Schlumberger', alpha=0.5)
technip['returns'].plot(kind = 'kde', label ='Technip', alpha=0.5)
valero['returns'].plot(kind = 'kde', label ='Valero', alpha=0.5)
williams['returns'].plot(kind = 'kde', label ='Williams', alpha=0.5)
plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [31]:
#Boxplots comparing returns for Energy Sector
box_df = pd.concat([sp['returns'],apache['returns'],baker['returns'],cabot['returns'],chevron['returns'],concho['returns'],conoco['returns'],devon['returns'],diamond['returns'],EOG['returns'],exxon['returns'],halliburton['returns'],hess['returns'],holly['returns'],kinder['returns'],marathonoil['returns'],marathonpet['returns'],national['returns'],noble['returns'],occidental['returns'],oneok['returns'],phillips66['returns'],pioneer['returns'],schlumberger['returns'],technip['returns'],valero['returns'],williams['returns']], axis=1)
box_df.columns = ['S&P 500','Apache','Baker','Cabot','Chevron','Concho','Conoco','Devon','Diamond','EOG','Exxon','Halliburton','Hess','Holly','Kinder','MarathonOil','MarathonPet','National','Noble','Occidental','Oneok','Phillips66','Pioneer','Schlumberger','Technip','Valero','Williams']
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Energy Sector Returns')
Out[31]:
<matplotlib.axes._subplots.AxesSubplot at 0x13f98162630>
In [32]:
#Cumulative Returns Whole period

sp['Cumulative Return'].plot(label="S&P 500")
apache['Cumulative Return'].plot(label='Apache', figsize=(16,8), title = 'Cumulative Return of Energy Stocks')
baker['Cumulative Return'].plot(label="Baker")
cabot['Cumulative Return'].plot(label="Cabot")
chevron['Cumulative Return'].plot(label="Chevron")
concho['Cumulative Return'].plot(label="Concho")
conoco['Cumulative Return'].plot(label="Conoco")
devon['Cumulative Return'].plot(label="Devon")
diamond['Cumulative Return'].plot(label="Diamond")
EOG['Cumulative Return'].plot(label="EOG")
exxon['Cumulative Return'].plot(label="Exxon")
halliburton['Cumulative Return'].plot(label="Halliburton")
hess['Cumulative Return'].plot(label="Hess")
holly['Cumulative Return'].plot(label="Holly")
kinder['Cumulative Return'].plot(label="Kinder")
marathonoil['Cumulative Return'].plot(label="MarathonOil")
marathonpet['Cumulative Return'].plot(label="MarathonPet")
national['Cumulative Return'].plot(label="National")
noble['Cumulative Return'].plot(label="Noble")
occidental['Cumulative Return'].plot(label="Occidental")
oneok['Cumulative Return'].plot(label="Oneok")
phillips66['Cumulative Return'].plot(label="Phillip66")
pioneer['Cumulative Return'].plot(label="Pioneer")
schlumberger['Cumulative Return'].plot(label="Schlemberger")
technip['Cumulative Return'].plot(label="Technip")
valero['Cumulative Return'].plot(label="Valero")
williams['Cumulative Return'].plot(label="Williams")

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [33]:
#Sorting by Cumulative Return


cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],apache['Cumulative Return'].iloc[-1],baker['Cumulative Return'].iloc[-1],cabot['Cumulative Return'].iloc[-1],chevron['Cumulative Return'].iloc[-1],concho['Cumulative Return'].iloc[-1],conoco['Cumulative Return'].iloc[-1],devon['Cumulative Return'].iloc[-1],diamond['Cumulative Return'].iloc[-1],EOG['Cumulative Return'].iloc[-1],exxon['Cumulative Return'].iloc[-1],halliburton['Cumulative Return'].iloc[-1],hess['Cumulative Return'].iloc[-1],holly['Cumulative Return'].iloc[-1],kinder['Cumulative Return'].iloc[-1],marathonoil['Cumulative Return'].iloc[-1],marathonpet['Cumulative Return'].iloc[-1],national['Cumulative Return'].iloc[-1],noble['Cumulative Return'].iloc[-1],occidental['Cumulative Return'].iloc[-1],oneok['Cumulative Return'].iloc[-1],phillips66['Cumulative Return'].iloc[-1],pioneer['Cumulative Return'].iloc[-1],schlumberger['Cumulative Return'].iloc[-1],technip['Cumulative Return'].iloc[-1],valero['Cumulative Return'].iloc[-1],williams['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],apachebef['Cumulative Return Before Crash'].iloc[-1],bakerbef['Cumulative Return Before Crash'].iloc[-1],cabotbef['Cumulative Return Before Crash'].iloc[-1],chevronbef['Cumulative Return Before Crash'].iloc[-1],conchobef['Cumulative Return Before Crash'].iloc[-1],conocobef['Cumulative Return Before Crash'].iloc[-1],devonbef['Cumulative Return Before Crash'].iloc[-1],diamondbef['Cumulative Return Before Crash'].iloc[-1],EOGbef['Cumulative Return Before Crash'].iloc[-1],exxonbef['Cumulative Return Before Crash'].iloc[-1],halliburtonbef['Cumulative Return Before Crash'].iloc[-1],hessbef['Cumulative Return Before Crash'].iloc[-1],hollybef['Cumulative Return Before Crash'].iloc[-1],kinderbef['Cumulative Return Before Crash'].iloc[-1],marathonoilbef['Cumulative Return Before Crash'].iloc[-1],marathonpetbef['Cumulative Return Before Crash'].iloc[-1],nationalbef['Cumulative Return Before Crash'].iloc[-1],noblebef['Cumulative Return Before Crash'].iloc[-1],occidentalbef['Cumulative Return Before Crash'].iloc[-1],oneokbef['Cumulative Return Before Crash'].iloc[-1],phillips66bef['Cumulative Return Before Crash'].iloc[-1],pioneerbef['Cumulative Return Before Crash'].iloc[-1],schlumbergerbef['Cumulative Return Before Crash'].iloc[-1],technipbef['Cumulative Return Before Crash'].iloc[-1],valerobef['Cumulative Return Before Crash'].iloc[-1],williamsbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],apachedur['Cumulative Return During Crash'].iloc[-1],bakerdur['Cumulative Return During Crash'].iloc[-1],cabotdur['Cumulative Return During Crash'].iloc[-1],chevrondur['Cumulative Return During Crash'].iloc[-1],conchodur['Cumulative Return During Crash'].iloc[-1],conocodur['Cumulative Return During Crash'].iloc[-1],devondur['Cumulative Return During Crash'].iloc[-1],diamonddur['Cumulative Return During Crash'].iloc[-1],EOGdur['Cumulative Return During Crash'].iloc[-1],exxondur['Cumulative Return During Crash'].iloc[-1],halliburtondur['Cumulative Return During Crash'].iloc[-1],hessdur['Cumulative Return During Crash'].iloc[-1],hollydur['Cumulative Return During Crash'].iloc[-1],kinderdur['Cumulative Return During Crash'].iloc[-1],marathonoildur['Cumulative Return During Crash'].iloc[-1],marathonpetdur['Cumulative Return During Crash'].iloc[-1],nationaldur['Cumulative Return During Crash'].iloc[-1],nobledur['Cumulative Return During Crash'].iloc[-1],occidentaldur['Cumulative Return During Crash'].iloc[-1],oneokdur['Cumulative Return During Crash'].iloc[-1],phillips66dur['Cumulative Return During Crash'].iloc[-1],pioneerdur['Cumulative Return During Crash'].iloc[-1],schlumbergerdur['Cumulative Return During Crash'].iloc[-1],technipdur['Cumulative Return During Crash'].iloc[-1],valerodur['Cumulative Return During Crash'].iloc[-1],williamsdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],apacheaft['Cumulative Return After Crash'].iloc[-1],bakeraft['Cumulative Return After Crash'].iloc[-1],cabotaft['Cumulative Return After Crash'].iloc[-1],chevronaft['Cumulative Return After Crash'].iloc[-1],conchoaft['Cumulative Return After Crash'].iloc[-1],conocoaft['Cumulative Return After Crash'].iloc[-1],devonaft['Cumulative Return After Crash'].iloc[-1],diamondaft['Cumulative Return After Crash'].iloc[-1],EOGaft['Cumulative Return After Crash'].iloc[-1],exxonaft['Cumulative Return After Crash'].iloc[-1],halliburtonaft['Cumulative Return After Crash'].iloc[-1],hessaft['Cumulative Return After Crash'].iloc[-1],hollyaft['Cumulative Return After Crash'].iloc[-1],kinderaft['Cumulative Return After Crash'].iloc[-1],marathonoilaft['Cumulative Return After Crash'].iloc[-1],marathonpetaft['Cumulative Return After Crash'].iloc[-1],nationalaft['Cumulative Return After Crash'].iloc[-1],nobleaft['Cumulative Return After Crash'].iloc[-1],occidentalaft['Cumulative Return After Crash'].iloc[-1],oneokaft['Cumulative Return After Crash'].iloc[-1],phillips66aft['Cumulative Return After Crash'].iloc[-1],pioneeraft['Cumulative Return After Crash'].iloc[-1],schlumbergeraft['Cumulative Return After Crash'].iloc[-1],technipaft['Cumulative Return After Crash'].iloc[-1],valeroaft['Cumulative Return After Crash'].iloc[-1],williamsaft['Cumulative Return After Crash'].iloc[-1]]}                         

df2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','Apache','Baker','Cabot','Chevron','Concho','Conoco','Devon','Diamond','EOG','Exxon','Halliburton','Hess','Holly','Kinder','MarathonOil','MarathonPet','National','Noble','Occidental','Oneok','Phillips66','Pioneer','Schlumberger','Technip','Valero','Williams'])

pd.DataFrame(df2)
Out[33]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
Apache 0.859358 1.528342 0.150805 3.728538
Baker 0.800631 0.931890 0.451597 1.902465
Cabot 1.268323 0.953416 0.921173 1.444130
Chevron 0.862066 0.939029 0.493762 1.859277
Concho 0.950866 1.100975 0.467544 1.847226
Conoco 0.801418 0.971136 0.416950 1.979226
Devon 0.652077 0.989281 0.279007 2.362460
Diamond 0.648088 0.974181 0.259557 2.563083
EOG 0.823178 1.054684 0.454534 1.717140
Exxon 0.773197 0.871959 0.525393 1.687758
Halliburton 0.676293 1.022357 0.238724 2.770993
Hess 0.931558 1.057171 0.446693 1.972674
Holly 0.724104 0.839110 0.437925 1.970525
Kinder 0.866321 1.152332 0.495953 1.515866
MarathonOil 0.601479 0.842235 0.326829 2.185075
MarathonPet 0.692321 1.003831 0.275805 2.500602
National 0.660659 1.038242 0.412786 1.541538
Noble 0.538498 0.887135 0.240970 2.519016
Occidental 0.540702 1.117555 0.225506 2.145511
Oneok 0.640265 1.094451 0.252838 2.313775
Phillips66 0.776406 0.801190 0.466681 2.076503
Pioneer 0.849667 1.092100 0.430015 1.809267
Schlumberger 0.607626 0.934789 0.379249 1.713952
Technip 0.500783 0.900261 0.365429 1.522222
Valero 0.800772 0.903015 0.387549 2.288167
Williams 0.955264 0.996385 0.505669 1.895964
In [34]:
df2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[34]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
Apache 0.859358 1.528342 0.150805 3.728538
Halliburton 0.676293 1.022357 0.238724 2.770993
Diamond 0.648088 0.974181 0.259557 2.563083
Noble 0.538498 0.887135 0.240970 2.519016
MarathonPet 0.692321 1.003831 0.275805 2.500602
Devon 0.652077 0.989281 0.279007 2.362460
Oneok 0.640265 1.094451 0.252838 2.313775
Valero 0.800772 0.903015 0.387549 2.288167
MarathonOil 0.601479 0.842235 0.326829 2.185075
Occidental 0.540702 1.117555 0.225506 2.145511
Phillips66 0.776406 0.801190 0.466681 2.076503
Conoco 0.801418 0.971136 0.416950 1.979226
Hess 0.931558 1.057171 0.446693 1.972674
Holly 0.724104 0.839110 0.437925 1.970525
Baker 0.800631 0.931890 0.451597 1.902465
Williams 0.955264 0.996385 0.505669 1.895964
Chevron 0.862066 0.939029 0.493762 1.859277
Concho 0.950866 1.100975 0.467544 1.847226
Pioneer 0.849667 1.092100 0.430015 1.809267
EOG 0.823178 1.054684 0.454534 1.717140
Schlumberger 0.607626 0.934789 0.379249 1.713952
Exxon 0.773197 0.871959 0.525393 1.687758
National 0.660659 1.038242 0.412786 1.541538
Technip 0.500783 0.900261 0.365429 1.522222
Kinder 0.866321 1.152332 0.495953 1.515866
Cabot 1.268323 0.953416 0.921173 1.444130
S&P 500 1.026077 1.083678 0.663281 1.427519
In [35]:
#Cumulative Returns Before Crash

spbef['Cumulative Return Before Crash'].plot(label="S&P 500")
apachebef['Cumulative Return Before Crash'].plot(label='Apache', figsize=(16,8), title = 'Cumulative Return of Energy Stocks Before Crash')
bakerbef['Cumulative Return Before Crash'].plot(label="Baker")
cabotbef['Cumulative Return Before Crash'].plot(label="Cabot")
chevronbef['Cumulative Return Before Crash'].plot(label="Chevron")
conchobef['Cumulative Return Before Crash'].plot(label="Concho")
conocobef['Cumulative Return Before Crash'].plot(label="Conoco")
devonbef['Cumulative Return Before Crash'].plot(label="Devon")
diamondbef['Cumulative Return Before Crash'].plot(label="Diamond")
EOGbef['Cumulative Return Before Crash'].plot(label="EOG")
exxonbef['Cumulative Return Before Crash'].plot(label="Exxon")
halliburtonbef['Cumulative Return Before Crash'].plot(label="Halliburton")
hessbef['Cumulative Return Before Crash'].plot(label="Hess")
hollybef['Cumulative Return Before Crash'].plot(label="Holly")
kinderbef['Cumulative Return Before Crash'].plot(label="Kinder")
marathonoilbef['Cumulative Return Before Crash'].plot(label="MarathonOil")
marathonpetbef['Cumulative Return Before Crash'].plot(label="MarathonPet")
nationalbef['Cumulative Return Before Crash'].plot(label="National")
noblebef['Cumulative Return Before Crash'].plot(label="Noble")
occidentalbef['Cumulative Return Before Crash'].plot(label="Occidental")
oneokbef['Cumulative Return Before Crash'].plot(label="Oneok")
phillips66bef['Cumulative Return Before Crash'].plot(label="Phillip66")
pioneerbef['Cumulative Return Before Crash'].plot(label="Pioneer")
schlumbergerbef['Cumulative Return Before Crash'].plot(label="Schlumberger")
technipbef['Cumulative Return Before Crash'].plot(label="Technip")
valerobef['Cumulative Return Before Crash'].plot(label="Valero")
williamsbef['Cumulative Return Before Crash'].plot(label="Williams")
plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [36]:
#Cumulative Returns During Crash

spdur['Cumulative Return During Crash'].plot(label="S&P 500")
apachedur['Cumulative Return During Crash'].plot(label='Apache', figsize=(16,8), title = 'Cumulative Return of Energy Stocks During Crash')
bakerdur['Cumulative Return During Crash'].plot(label="Baker")
cabotdur['Cumulative Return During Crash'].plot(label="Cabot")
chevrondur['Cumulative Return During Crash'].plot(label="Chevron")
conchodur['Cumulative Return During Crash'].plot(label="Concho")
conocodur['Cumulative Return During Crash'].plot(label="Conoco")
devondur['Cumulative Return During Crash'].plot(label="Devon")
diamonddur['Cumulative Return During Crash'].plot(label="Diamond")
EOGdur['Cumulative Return During Crash'].plot(label="EOG")
exxondur['Cumulative Return During Crash'].plot(label="Exxon")
halliburtondur['Cumulative Return During Crash'].plot(label="Halliburton")
hessdur['Cumulative Return During Crash'].plot(label="Hess")
hollydur['Cumulative Return During Crash'].plot(label="Holly")
kinderdur['Cumulative Return During Crash'].plot(label="Kinder")
marathonoildur['Cumulative Return During Crash'].plot(label="MarathonOil")
marathonpetdur['Cumulative Return During Crash'].plot(label="MarathonPet")
nationaldur['Cumulative Return During Crash'].plot(label="National")
nobledur['Cumulative Return During Crash'].plot(label="Noble")
occidentaldur['Cumulative Return During Crash'].plot(label="Occidental")
oneokdur['Cumulative Return During Crash'].plot(label="Oneok")
phillips66dur['Cumulative Return During Crash'].plot(label="Phillip66")
pioneerdur['Cumulative Return During Crash'].plot(label="Pioneer")
schlumbergerdur['Cumulative Return During Crash'].plot(label="Schlumberger")
technipdur['Cumulative Return During Crash'].plot(label="Technip")
valerodur['Cumulative Return During Crash'].plot(label="Valero")
williamsdur['Cumulative Return During Crash'].plot(label="Williams")
plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [37]:
#Cumulative Returns After Crash

spaft['Cumulative Return After Crash'].plot(label="S&P 500")
apacheaft['Cumulative Return After Crash'].plot(label='Apache', figsize=(16,8), title = 'Cumulative Return of Energy Stocks After Crash')
bakeraft['Cumulative Return After Crash'].plot(label="Baker")
cabotaft['Cumulative Return After Crash'].plot(label="Cabot")
chevronaft['Cumulative Return After Crash'].plot(label="Chevron")
conchoaft['Cumulative Return After Crash'].plot(label="Concho")
conocoaft['Cumulative Return After Crash'].plot(label="Conoco")
devonaft['Cumulative Return After Crash'].plot(label="Devon")
diamondaft['Cumulative Return After Crash'].plot(label="Diamond")
EOGaft['Cumulative Return After Crash'].plot(label="EOG")
exxonaft['Cumulative Return After Crash'].plot(label="Exxon")
halliburtonaft['Cumulative Return After Crash'].plot(label="Halliburton")
hessaft['Cumulative Return After Crash'].plot(label="Hess")
hollyaft['Cumulative Return After Crash'].plot(label="Holly")
kinderaft['Cumulative Return After Crash'].plot(label="Kinder")
marathonoilaft['Cumulative Return After Crash'].plot(label="MarathonOil")
marathonpetaft['Cumulative Return After Crash'].plot(label="MarathonPet")
nationalaft['Cumulative Return After Crash'].plot(label="National")
nobleaft['Cumulative Return After Crash'].plot(label="Noble")
occidentalaft['Cumulative Return After Crash'].plot(label="Occidental")
oneokaft['Cumulative Return After Crash'].plot(label="Oneok")
phillips66aft['Cumulative Return After Crash'].plot(label="Phillip66")
pioneeraft['Cumulative Return After Crash'].plot(label="Pioneer")
schlumbergeraft['Cumulative Return After Crash'].plot(label="Schlumberger")
technipaft['Cumulative Return After Crash'].plot(label="Technip")
valeroaft['Cumulative Return After Crash'].plot(label="Valero")
williamsaft['Cumulative Return After Crash'].plot(label="Williams")
plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [38]:
#Table of Key Statistics of Energy Stocks
import pandas as pd
import statistics as stats

means = [apachemean,bakermean,cabotmean,chevronmean,conchomean,conocomean,devonmean,diamondmean,EOGmean,exxonmean,halliburtonmean,hessmean,hollymean,kindermean,marathonoilmean,marathonpetmean,nationalmean,noblemean,occidentalmean,oneokmean,phillips66mean,pioneermean,schlumbergermean,technipmean,valeromean,williamsmean]
averagemean = stats.mean(means)
variances = [apachevar,bakervar,cabotvar,chevronvar,conchovar,conocovar,devonvar,diamondvar,EOGvar,exxonvar,halliburtonvar,hessvar,hollyvar,kindervar,marathonoilvar,marathonpetvar,nationalvar,noblevar,occidentalvar,oneokvar,phillips66var,pioneervar,schlumbergervar,technipvar,valerovar,williamsvar]
averagevar = stats.mean(variances)
kurtoses = [apachekurt,bakerkurt,cabotkurt,chevronkurt,conchokurt,conocokurt,devonkurt,diamondkurt,EOGkurt,exxonkurt,halliburtonkurt,hesskurt,hollykurt,kinderkurt,marathonoilkurt,marathonpetkurt,nationalkurt,noblekurt,occidentalkurt,oneokkurt,phillips66kurt,pioneerkurt,schlumbergerkurt,technipkurt,valerokurt,williamskurt]
averagekurt = stats.mean(kurtoses)
skews = [apacheskew,bakerskew,cabotskew,chevronskew,conchoskew,conocoskew,devonskew,diamondskew,EOGskew,exxonskew,halliburtonskew,hessskew,hollyskew,kinderskew,marathonoilskew,marathonpetskew,nationalskew,nobleskew,occidentalskew,oneokskew,phillips66skew,pioneerskew,schlumbergerskew,technipskew,valeroskew,williamsskew]
averageskew = stats.mean(skews)

Energy = {'Mean': [[spmean],[averagemean],[apachemean], [bakermean],[cabotmean],[chevronmean],[conchomean],[conocomean],[devonmean],[diamondmean],[EOGmean],[exxonmean],[halliburtonmean],[hessmean],[hollymean],[kindermean],[marathonoilmean],[marathonpetmean],[nationalmean],[noblemean],[occidentalmean],[oneokmean],[phillips66mean],[pioneermean],[schlumbergermean],[technipmean],[valeromean],[williamsmean]], 
        'Variance': [[spvar],[averagevar],[apachevar],[bakervar],[cabotvar],[chevronvar],[conchovar],[conocovar],[devonvar],[diamondvar],[EOGvar],[exxonvar],[halliburtonvar],[hessvar],[hollyvar],[kindervar],[marathonoilvar],[marathonpetvar],[nationalvar],[noblevar],[occidentalvar],[oneokvar],[phillips66var],[pioneervar],[schlumbergervar],[technipvar],[valerovar],[williamsvar]], 
          'Kurtosis': [[spkurt],[averagekurt],[apachekurt],[bakerkurt],[cabotkurt],[chevronkurt],[conchokurt],[conocokurt],[devonkurt],[diamondkurt],[EOGkurt],[exxonkurt],[halliburtonkurt],[hesskurt],[hollykurt],[kinderkurt],[marathonoilkurt],[marathonpetkurt],[nationalkurt],[noblekurt],[occidentalkurt],[oneokkurt],[phillips66kurt],[pioneerkurt],[schlumbergerkurt],[technipkurt],[valerokurt],[williamskurt]],
          'Skew': [[spskew],[averageskew],[apacheskew],[bakerskew],[cabotskew],[chevronskew],[conchoskew],[conocoskew],[devonskew],[diamondskew],[EOGskew],[exxonskew],[halliburtonskew],[hessskew],[hollyskew],[kinderskew],[marathonoilskew],[marathonpetskew],[nationalskew],[nobleskew],[occidentalskew],[oneokskew],[phillips66skew],[pioneerskew],[schlumbergerskew],[technipskew],[valeroskew],[williamsskew]],
        }

df = pd.DataFrame(Energy, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','Apache','Baker','Cabot','Chevron','Concho','Conoco','Devon','Diamond','EOG','Exxon','Halliburton','Hess','Holly','Kinder','MarathonOil','MarathonPet','National','Noble','Occidental','Oneok','Phillips66','Pioneer','Schlumberger','Technip','Valero','Williams']
)

pd.DataFrame(df)
Out[38]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [-0.000269796662685561] [0.003880462737369595] [6.9688586637195975] [-0.5619505120891912]
Apache [0.004150100607343733] [0.009336117364461036] [9.126365059169988] [-1.257527317358858]
Baker [-0.0003523935635848395] [0.0028264094053810137] [4.908597071437032] [0.12666782302745583]
Cabot [0.0026122470866814696] [0.0014850178523556487] [3.726939956777649] [0.7438535102047723]
Chevron [-3.8678930179897054e-05] [0.002244097936198929] [8.726217435609737] [-0.3306980734773935]
Concho [0.000893137668970244] [0.002617132255326157] [3.231218076789017] [0.3090611903115926]
Conoco [-0.0002525425679946991] [0.0029851523159863747] [7.284292167493752] [-0.005623407210922443]
Devon [-0.0006156530659074831] [0.005264444808681759] [6.064565277904928] [-0.7013714379501274]
Diamond [-0.00036035268613299285] [0.005495911282862696] [10.82231766757786] [-1.5730749009696263]
EOG [2.7294716369071224e-05] [0.0029549768077119145] [9.874175199700236] [-1.3760337966511187]
Exxon [-0.0013291639498119276] [0.001424647758597483] [2.120204391883129] [-0.017433029425563355]
Halliburton [-0.0003951749767760048] [0.005043072832386709] [8.202479692107971] [-1.0260817524742414]
Hess [0.0012562285918953566] [0.0034747298840493743] [9.44568464428204] [-0.8207005234704058]
Holly [-0.0010830298770465852] [0.003000312303962981] [2.130336225596161] [0.44519322265332584]
Kinder [-0.00014435654075058168] [0.0019265778769164386] [7.5074987195951985] [-1.0307624766420027]
MarathonOil [-0.0012811244217946973] [0.0047756901895654345] [16.711559213475226] [-1.97242084839137]
MarathonPet [-0.0007994473512900348] [0.004151517054261181] [3.562183356391089] [-0.29979304904774556]
National [-0.0013020526190698754] [0.003863846657581698] [4.515535060423933] [-0.442644075026557]
Noble [-0.0013316731022823885] [0.006969490685798572] [4.9547678200621705] [-0.08489675527368055]
Occidental [-0.0009834012206630762] [0.006632297838889853] [16.256049212651924] [-1.5765812783063926]
Oneok [-0.000639772390656009] [0.00533528792557648] [10.102371893460585] [-1.1191997062612724]
Phillips66 [-0.0008366122116410136] [0.0023488588970982404] [2.2599208627896896] [0.06650901034493552]
Pioneer [0.00070242494324664] [0.0037052660624838606] [11.902470883686725] [-1.3116773679767926]
Schlumberger [-0.0022901087519066397] [0.0032085217799812434] [4.640124633801943] [-0.6766841451197311]
Technip [-0.0033559980282769357] [0.004144493107038483] [3.690320447491832] [-0.3076917193524641]
Valero [-0.0002525520948022351] [0.003020907175530506] [2.6170540369783266] [0.021433975936404785]
Williams [0.0009879415062368163] [0.0026572531129254043] [6.807076249571385] [-0.3925363864111937]
In [39]:
df.sort_values("Mean", ascending=False)
Out[39]:
Mean Variance Kurtosis Skew
Apache [0.004150100607343733] [0.009336117364461036] [9.126365059169988] [-1.257527317358858]
Cabot [0.0026122470866814696] [0.0014850178523556487] [3.726939956777649] [0.7438535102047723]
Hess [0.0012562285918953566] [0.0034747298840493743] [9.44568464428204] [-0.8207005234704058]
Williams [0.0009879415062368163] [0.0026572531129254043] [6.807076249571385] [-0.3925363864111937]
Concho [0.000893137668970244] [0.002617132255326157] [3.231218076789017] [0.3090611903115926]
Pioneer [0.00070242494324664] [0.0037052660624838606] [11.902470883686725] [-1.3116773679767926]
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
EOG [2.7294716369071224e-05] [0.0029549768077119145] [9.874175199700236] [-1.3760337966511187]
Chevron [-3.8678930179897054e-05] [0.002244097936198929] [8.726217435609737] [-0.3306980734773935]
Kinder [-0.00014435654075058168] [0.0019265778769164386] [7.5074987195951985] [-1.0307624766420027]
Conoco [-0.0002525425679946991] [0.0029851523159863747] [7.284292167493752] [-0.005623407210922443]
Valero [-0.0002525520948022351] [0.003020907175530506] [2.6170540369783266] [0.021433975936404785]
Average [-0.000269796662685561] [0.003880462737369595] [6.9688586637195975] [-0.5619505120891912]
Baker [-0.0003523935635848395] [0.0028264094053810137] [4.908597071437032] [0.12666782302745583]
Diamond [-0.00036035268613299285] [0.005495911282862696] [10.82231766757786] [-1.5730749009696263]
Halliburton [-0.0003951749767760048] [0.005043072832386709] [8.202479692107971] [-1.0260817524742414]
Devon [-0.0006156530659074831] [0.005264444808681759] [6.064565277904928] [-0.7013714379501274]
Oneok [-0.000639772390656009] [0.00533528792557648] [10.102371893460585] [-1.1191997062612724]
MarathonPet [-0.0007994473512900348] [0.004151517054261181] [3.562183356391089] [-0.29979304904774556]
Phillips66 [-0.0008366122116410136] [0.0023488588970982404] [2.2599208627896896] [0.06650901034493552]
Occidental [-0.0009834012206630762] [0.006632297838889853] [16.256049212651924] [-1.5765812783063926]
Holly [-0.0010830298770465852] [0.003000312303962981] [2.130336225596161] [0.44519322265332584]
MarathonOil [-0.0012811244217946973] [0.0047756901895654345] [16.711559213475226] [-1.97242084839137]
National [-0.0013020526190698754] [0.003863846657581698] [4.515535060423933] [-0.442644075026557]
Exxon [-0.0013291639498119276] [0.001424647758597483] [2.120204391883129] [-0.017433029425563355]
Noble [-0.0013316731022823885] [0.006969490685798572] [4.9547678200621705] [-0.08489675527368055]
Schlumberger [-0.0022901087519066397] [0.0032085217799812434] [4.640124633801943] [-0.6766841451197311]
Technip [-0.0033559980282769357] [0.004144493107038483] [3.690320447491832] [-0.3076917193524641]

Materials Stocks

In [40]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
APD = web.DataReader('APD', 'yahoo', start, end)
APD['returns'] = APD['Close'].pct_change(1)
APD['Cumulative Return'] = (1 + APD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
APDmean = APD['returns'].mean()
APDvar = APD['returns'].var()
APDkurt = APD['returns'].kurt()
APDskew = APD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
APDbef = web.DataReader('APD', 'yahoo', start, crashstart)
APDbef['avgreturnbefore'] = APDbef['Close'].pct_change(1)
APDbef['Cumulative Return Before Crash'] = (1 + APDbef['avgreturnbefore']).cumprod()

#period of crash
APDdur = web.DataReader('APD', 'yahoo', crashstart, crashend)
APDdur['avgreturnduring'] = APDdur['Close'].pct_change(1)
APDdur['Cumulative Return During Crash'] = (1 + APDdur['avgreturnduring']).cumprod()

#period after crash
APDaft = web.DataReader('APD', 'yahoo', crashend, end)
APDaft['avgreturnafter'] = APDaft['Close'].pct_change(1)
APDaft['Cumulative Return After Crash'] = (1 + APDaft['avgreturnafter']).cumprod()
In [41]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ALB = web.DataReader('ALB', 'yahoo', start, end)
ALB['returns'] = ALB['Close'].pct_change(1)
ALB['Cumulative Return'] = (1 + ALB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ALBmean = ALB['returns'].mean()
ALBvar = ALB['returns'].var()
ALBkurt = ALB['returns'].kurt()
ALBskew = ALB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ALBbef = web.DataReader('ALB', 'yahoo', start, crashstart)
ALBbef['avgreturnbefore'] = ALBbef['Close'].pct_change(1)
ALBbef['Cumulative Return Before Crash'] = (1 + ALBbef['avgreturnbefore']).cumprod()

#period of crash
ALBdur = web.DataReader('ALB', 'yahoo', crashstart, crashend)
ALBdur['avgreturnduring'] = ALBdur['Close'].pct_change(1)
ALBdur['Cumulative Return During Crash'] = (1 + ALBdur['avgreturnduring']).cumprod()

#period after crash
ALBaft = web.DataReader('ALB', 'yahoo', crashend, end)
ALBaft['avgreturnafter'] = ALBaft['Close'].pct_change(1)
ALBaft['Cumulative Return After Crash'] = (1 + ALBaft['avgreturnafter']).cumprod()
In [42]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AMCR = web.DataReader('AMCR', 'yahoo', start, end)
AMCR['returns'] = AMCR['Close'].pct_change(1)
AMCR['Cumulative Return'] = (1 + AMCR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AMCRmean = AMCR['returns'].mean()
AMCRvar = AMCR['returns'].var()
AMCRkurt = AMCR['returns'].kurt()
AMCRskew = AMCR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AMCRbef = web.DataReader('AMCR', 'yahoo', start, crashstart)
AMCRbef['avgreturnbefore'] = AMCRbef['Close'].pct_change(1)
AMCRbef['Cumulative Return Before Crash'] = (1 + AMCRbef['avgreturnbefore']).cumprod()

#period of crash
AMCRdur = web.DataReader('AMCR', 'yahoo', crashstart, crashend)
AMCRdur['avgreturnduring'] = AMCRdur['Close'].pct_change(1)
AMCRdur['Cumulative Return During Crash'] = (1 + AMCRdur['avgreturnduring']).cumprod()

#period after crash
AMCRaft = web.DataReader('AMCR', 'yahoo', crashend, end)
AMCRaft['avgreturnafter'] = AMCRaft['Close'].pct_change(1)
AMCRaft['Cumulative Return After Crash'] = (1 + AMCRaft['avgreturnafter']).cumprod()
In [43]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AVY = web.DataReader('AVY', 'yahoo', start, end)
AVY['returns'] = AVY['Close'].pct_change(1)
AVY['Cumulative Return'] = (1 + AVY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AVYmean = AVY['returns'].mean()
AVYvar = AVY['returns'].var()
AVYkurt = AVY['returns'].kurt()
AVYskew = AVY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AVYbef = web.DataReader('AVY', 'yahoo', start, crashstart)
AVYbef['avgreturnbefore'] = AVYbef['Close'].pct_change(1)
AVYbef['Cumulative Return Before Crash'] = (1 + AVYbef['avgreturnbefore']).cumprod()

#period of crash
AVYdur = web.DataReader('AVY', 'yahoo', crashstart, crashend)
AVYdur['avgreturnduring'] = AVYdur['Close'].pct_change(1)
AVYdur['Cumulative Return During Crash'] = (1 + AVYdur['avgreturnduring']).cumprod()

#period after crash
AVYaft = web.DataReader('AVY', 'yahoo', crashend, end)
AVYaft['avgreturnafter'] = AVYaft['Close'].pct_change(1)
AVYaft['Cumulative Return After Crash'] = (1 + AVYaft['avgreturnafter']).cumprod()
In [44]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BLL = web.DataReader('BLL', 'yahoo', start, end)
BLL['returns'] = BLL['Close'].pct_change(1)
BLL['Cumulative Return'] = (1 + BLL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BLLmean = BLL['returns'].mean()
BLLvar = BLL['returns'].var()
BLLkurt = BLL['returns'].kurt()
BLLskew = BLL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BLLbef = web.DataReader('BLL', 'yahoo', start, crashstart)
BLLbef['avgreturnbefore'] = BLLbef['Close'].pct_change(1)
BLLbef['Cumulative Return Before Crash'] = (1 + BLLbef['avgreturnbefore']).cumprod()

#period of crash
BLLdur = web.DataReader('BLL', 'yahoo', crashstart, crashend)
BLLdur['avgreturnduring'] = BLLdur['Close'].pct_change(1)
BLLdur['Cumulative Return During Crash'] = (1 + BLLdur['avgreturnduring']).cumprod()

#period after crash
BLLaft = web.DataReader('BLL', 'yahoo', crashend, end)
BLLaft['avgreturnafter'] = BLLaft['Close'].pct_change(1)
BLLaft['Cumulative Return After Crash'] = (1 + BLLaft['avgreturnafter']).cumprod()
In [45]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CE = web.DataReader('CE', 'yahoo', start, end)
CE['returns'] = CE['Close'].pct_change(1)
CE['Cumulative Return'] = (1 + CE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CEmean = CE['returns'].mean()
CEvar = CE['returns'].var()
CEkurt = CE['returns'].kurt()
CEskew = CE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CEbef = web.DataReader('CE', 'yahoo', start, crashstart)
CEbef['avgreturnbefore'] = CEbef['Close'].pct_change(1)
CEbef['Cumulative Return Before Crash'] = (1 + CEbef['avgreturnbefore']).cumprod()

#period of crash
CEdur = web.DataReader('CE', 'yahoo', crashstart, crashend)
CEdur['avgreturnduring'] = CEdur['Close'].pct_change(1)
CEdur['Cumulative Return During Crash'] = (1 + CEdur['avgreturnduring']).cumprod()

#period after crash
CEaft = web.DataReader('CE', 'yahoo', crashend, end)
CEaft['avgreturnafter'] = CEaft['Close'].pct_change(1)
CEaft['Cumulative Return After Crash'] = (1 + CEaft['avgreturnafter']).cumprod()
In [46]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CF = web.DataReader('CF', 'yahoo', start, end)
CF['returns'] = CF['Close'].pct_change(1)
CF['Cumulative Return'] = (1 + CF['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CFmean = CF['returns'].mean()
CFvar = CF['returns'].var()
CFkurt = CF['returns'].kurt()
CFskew = CF['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CFbef = web.DataReader('CF', 'yahoo', start, crashstart)
CFbef['avgreturnbefore'] = CFbef['Close'].pct_change(1)
CFbef['Cumulative Return Before Crash'] = (1 + CFbef['avgreturnbefore']).cumprod()

#period of crash
CFdur = web.DataReader('CF', 'yahoo', crashstart, crashend)
CFdur['avgreturnduring'] = CFdur['Close'].pct_change(1)
CFdur['Cumulative Return During Crash'] = (1 + CFdur['avgreturnduring']).cumprod()

#period after crash
CFaft = web.DataReader('CF', 'yahoo', crashend, end)
CFaft['avgreturnafter'] = CFaft['Close'].pct_change(1)
CFaft['Cumulative Return After Crash'] = (1 + CFaft['avgreturnafter']).cumprod()
In [47]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CTVA = web.DataReader('CTVA', 'yahoo', start, end)
CTVA['returns'] = CTVA['Close'].pct_change(1)
CTVA['Cumulative Return'] = (1 + CTVA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CTVAmean = CTVA['returns'].mean()
CTVAvar = CTVA['returns'].var()
CTVAkurt = CTVA['returns'].kurt()
CTVAskew = CTVA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CTVAbef = web.DataReader('CTVA', 'yahoo', start, crashstart)
CTVAbef['avgreturnbefore'] = CTVAbef['Close'].pct_change(1)
CTVAbef['Cumulative Return Before Crash'] = (1 + CTVAbef['avgreturnbefore']).cumprod()

#period of crash
CTVAdur = web.DataReader('CTVA', 'yahoo', crashstart, crashend)
CTVAdur['avgreturnduring'] = CTVAdur['Close'].pct_change(1)
CTVAdur['Cumulative Return During Crash'] = (1 + CTVAdur['avgreturnduring']).cumprod()

#period after crash
CTVAaft = web.DataReader('CTVA', 'yahoo', crashend, end)
CTVAaft['avgreturnafter'] = CTVAaft['Close'].pct_change(1)
CTVAaft['Cumulative Return After Crash'] = (1 + CTVAaft['avgreturnafter']).cumprod()
In [48]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DOW = web.DataReader('DOW', 'yahoo', start, end)
DOW['returns'] = DOW['Close'].pct_change(1)
DOW['Cumulative Return'] = (1 + DOW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DOWmean = DOW['returns'].mean()
DOWvar = DOW['returns'].var()
DOWkurt = DOW['returns'].kurt()
DOWskew = DOW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DOWbef = web.DataReader('DOW', 'yahoo', start, crashstart)
DOWbef['avgreturnbefore'] = DOWbef['Close'].pct_change(1)
DOWbef['Cumulative Return Before Crash'] = (1 + DOWbef['avgreturnbefore']).cumprod()

#period of crash
DOWdur = web.DataReader('DOW', 'yahoo', crashstart, crashend)
DOWdur['avgreturnduring'] = DOWdur['Close'].pct_change(1)
DOWdur['Cumulative Return During Crash'] = (1 + DOWdur['avgreturnduring']).cumprod()

#period after crash
DOWaft = web.DataReader('DOW', 'yahoo', crashend, end)
DOWaft['avgreturnafter'] = DOWaft['Close'].pct_change(1)
DOWaft['Cumulative Return After Crash'] = (1 + DOWaft['avgreturnafter']).cumprod()
In [49]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DD = web.DataReader('DD', 'yahoo', start, end)
DD['returns'] = DD['Close'].pct_change(1)
DD['Cumulative Return'] = (1 + DD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DDmean = DD['returns'].mean()
DDvar = DD['returns'].var()
DDkurt = DD['returns'].kurt()
DDskew = DD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DDbef = web.DataReader('DD', 'yahoo', start, crashstart)
DDbef['avgreturnbefore'] = DDbef['Close'].pct_change(1)
DDbef['Cumulative Return Before Crash'] = (1 + DDbef['avgreturnbefore']).cumprod()

#period of crash
DDdur = web.DataReader('DD', 'yahoo', crashstart, crashend)
DDdur['avgreturnduring'] = DDdur['Close'].pct_change(1)
DDdur['Cumulative Return During Crash'] = (1 + DDdur['avgreturnduring']).cumprod()

#period after crash
DDaft = web.DataReader('DD', 'yahoo', crashend, end)
DDaft['avgreturnafter'] = DDaft['Close'].pct_change(1)
DDaft['Cumulative Return After Crash'] = (1 + DDaft['avgreturnafter']).cumprod()
In [50]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EMN = web.DataReader('EMN', 'yahoo', start, end)
EMN['returns'] = EMN['Close'].pct_change(1)
EMN['Cumulative Return'] = (1 + EMN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EMNmean = EMN['returns'].mean()
EMNvar = EMN['returns'].var()
EMNkurt = EMN['returns'].kurt()
EMNskew = EMN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EMNbef = web.DataReader('EMN', 'yahoo', start, crashstart)
EMNbef['avgreturnbefore'] = EMNbef['Close'].pct_change(1)
EMNbef['Cumulative Return Before Crash'] = (1 + EMNbef['avgreturnbefore']).cumprod()

#period of crash
EMNdur = web.DataReader('EMN', 'yahoo', crashstart, crashend)
EMNdur['avgreturnduring'] = EMNdur['Close'].pct_change(1)
EMNdur['Cumulative Return During Crash'] = (1 + EMNdur['avgreturnduring']).cumprod()

#period after crash
EMNaft = web.DataReader('EMN', 'yahoo', crashend, end)
EMNaft['avgreturnafter'] = EMNaft['Close'].pct_change(1)
EMNaft['Cumulative Return After Crash'] = (1 + EMNaft['avgreturnafter']).cumprod()
In [51]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ECL = web.DataReader('ECL', 'yahoo', start, end)
ECL['returns'] = ECL['Close'].pct_change(1)
ECL['Cumulative Return'] = (1 + ECL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ECLmean = ECL['returns'].mean()
ECLvar = ECL['returns'].var()
ECLkurt = ECL['returns'].kurt()
ECLskew = ECL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ECLbef = web.DataReader('ECL', 'yahoo', start, crashstart)
ECLbef['avgreturnbefore'] = ECLbef['Close'].pct_change(1)
ECLbef['Cumulative Return Before Crash'] = (1 + ECLbef['avgreturnbefore']).cumprod()

#period of crash
ECLdur = web.DataReader('ECL', 'yahoo', crashstart, crashend)
ECLdur['avgreturnduring'] = ECLdur['Close'].pct_change(1)
ECLdur['Cumulative Return During Crash'] = (1 + ECLdur['avgreturnduring']).cumprod()

#period after crash
ECLaft = web.DataReader('ECL', 'yahoo', crashend, end)
ECLaft['avgreturnafter'] = ECLaft['Close'].pct_change(1)
ECLaft['Cumulative Return After Crash'] = (1 + ECLaft['avgreturnafter']).cumprod()
In [52]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FMC = web.DataReader('FMC', 'yahoo', start, end)
FMC['returns'] = FMC['Close'].pct_change(1)
FMC['Cumulative Return'] = (1 + FMC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FMCmean = FMC['returns'].mean()
FMCvar = FMC['returns'].var()
FMCkurt = FMC['returns'].kurt()
FMCskew = FMC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FMCbef = web.DataReader('FMC', 'yahoo', start, crashstart)
FMCbef['avgreturnbefore'] = FMCbef['Close'].pct_change(1)
FMCbef['Cumulative Return Before Crash'] = (1 + FMCbef['avgreturnbefore']).cumprod()

#period of crash
FMCdur = web.DataReader('FMC', 'yahoo', crashstart, crashend)
FMCdur['avgreturnduring'] = FMCdur['Close'].pct_change(1)
FMCdur['Cumulative Return During Crash'] = (1 + FMCdur['avgreturnduring']).cumprod()

#period after crash
FMCaft = web.DataReader('FMC', 'yahoo', crashend, end)
FMCaft['avgreturnafter'] = FMCaft['Close'].pct_change(1)
FMCaft['Cumulative Return After Crash'] = (1 + FMCaft['avgreturnafter']).cumprod()
In [53]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FCX = web.DataReader('FCX', 'yahoo', start, end)
FCX['returns'] = FCX['Close'].pct_change(1)
FCX['Cumulative Return'] = (1 + FCX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FCXmean = FCX['returns'].mean()
FCXvar = FCX['returns'].var()
FCXkurt = FCX['returns'].kurt()
FCXskew = FCX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FCXbef = web.DataReader('FCX', 'yahoo', start, crashstart)
FCXbef['avgreturnbefore'] = FCXbef['Close'].pct_change(1)
FCXbef['Cumulative Return Before Crash'] = (1 + FCXbef['avgreturnbefore']).cumprod()

#period of crash
FCXdur = web.DataReader('FCX', 'yahoo', crashstart, crashend)
FCXdur['avgreturnduring'] = FCXdur['Close'].pct_change(1)
FCXdur['Cumulative Return During Crash'] = (1 + FCXdur['avgreturnduring']).cumprod()

#period after crash
FCXaft = web.DataReader('FCX', 'yahoo', crashend, end)
FCXaft['avgreturnafter'] = FCXaft['Close'].pct_change(1)
FCXaft['Cumulative Return After Crash'] = (1 + FCXaft['avgreturnafter']).cumprod()
In [54]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IP = web.DataReader('IP', 'yahoo', start, end)
IP['returns'] = IP['Close'].pct_change(1)
IP['Cumulative Return'] = (1 + IP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IPmean = IP['returns'].mean()
IPvar = IP['returns'].var()
IPkurt = IP['returns'].kurt()
IPskew = IP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IPbef = web.DataReader('IP', 'yahoo', start, crashstart)
IPbef['avgreturnbefore'] = IPbef['Close'].pct_change(1)
IPbef['Cumulative Return Before Crash'] = (1 + IPbef['avgreturnbefore']).cumprod()

#period of crash
IPdur = web.DataReader('IP', 'yahoo', crashstart, crashend)
IPdur['avgreturnduring'] = IPdur['Close'].pct_change(1)
IPdur['Cumulative Return During Crash'] = (1 + IPdur['avgreturnduring']).cumprod()

#period after crash
IPaft = web.DataReader('IP', 'yahoo', crashend, end)
IPaft['avgreturnafter'] = IPaft['Close'].pct_change(1)
IPaft['Cumulative Return After Crash'] = (1 + IPaft['avgreturnafter']).cumprod()
In [55]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IFF = web.DataReader('IFF', 'yahoo', start, end)
IFF['returns'] = IFF['Close'].pct_change(1)
IFF['Cumulative Return'] = (1 + IFF['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IFFmean = IFF['returns'].mean()
IFFvar = IFF['returns'].var()
IFFkurt = IFF['returns'].kurt()
IFFskew = IFF['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IFFbef = web.DataReader('IFF', 'yahoo', start, crashstart)
IFFbef['avgreturnbefore'] = IFFbef['Close'].pct_change(1)
IFFbef['Cumulative Return Before Crash'] = (1 + IFFbef['avgreturnbefore']).cumprod()

#period of crash
IFFdur = web.DataReader('IFF', 'yahoo', crashstart, crashend)
IFFdur['avgreturnduring'] = IFFdur['Close'].pct_change(1)
IFFdur['Cumulative Return During Crash'] = (1 + IFFdur['avgreturnduring']).cumprod()

#period after crash
IFFaft = web.DataReader('IFF', 'yahoo', crashend, end)
IFFaft['avgreturnafter'] = IFFaft['Close'].pct_change(1)
IFFaft['Cumulative Return After Crash'] = (1 + IFFaft['avgreturnafter']).cumprod()
In [56]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LIN = web.DataReader('LIN', 'yahoo', start, end)
LIN['returns'] = LIN['Close'].pct_change(1)
LIN['Cumulative Return'] = (1 + LIN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LINmean = LIN['returns'].mean()
LINvar = LIN['returns'].var()
LINkurt = LIN['returns'].kurt()
LINskew = LIN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LINbef = web.DataReader('LIN', 'yahoo', start, crashstart)
LINbef['avgreturnbefore'] = LINbef['Close'].pct_change(1)
LINbef['Cumulative Return Before Crash'] = (1 + LINbef['avgreturnbefore']).cumprod()

#period of crash
LINdur = web.DataReader('LIN', 'yahoo', crashstart, crashend)
LINdur['avgreturnduring'] = LINdur['Close'].pct_change(1)
LINdur['Cumulative Return During Crash'] = (1 + LINdur['avgreturnduring']).cumprod()

#period after crash
LINaft = web.DataReader('LIN', 'yahoo', crashend, end)
LINaft['avgreturnafter'] = LINaft['Close'].pct_change(1)
LINaft['Cumulative Return After Crash'] = (1 + LINaft['avgreturnafter']).cumprod()
In [57]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LYB = web.DataReader('LYB', 'yahoo', start, end)
LYB['returns'] = LYB['Close'].pct_change(1)
LYB['Cumulative Return'] = (1 + LYB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LYBmean = LYB['returns'].mean()
LYBvar = LYB['returns'].var()
LYBkurt = LYB['returns'].kurt()
LYBskew = LYB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LYBbef = web.DataReader('LYB', 'yahoo', start, crashstart)
LYBbef['avgreturnbefore'] = LYBbef['Close'].pct_change(1)
LYBbef['Cumulative Return Before Crash'] = (1 + LYBbef['avgreturnbefore']).cumprod()

#period of crash
LYBdur = web.DataReader('LYB', 'yahoo', crashstart, crashend)
LYBdur['avgreturnduring'] = LYBdur['Close'].pct_change(1)
LYBdur['Cumulative Return During Crash'] = (1 + LYBdur['avgreturnduring']).cumprod()

#period after crash
LYBaft = web.DataReader('LYB', 'yahoo', crashend, end)
LYBaft['avgreturnafter'] = LYBaft['Close'].pct_change(1)
LYBaft['Cumulative Return After Crash'] = (1 + LYBaft['avgreturnafter']).cumprod()
In [58]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MLM = web.DataReader('MLM', 'yahoo', start, end)
MLM['returns'] = MLM['Close'].pct_change(1)
MLM['Cumulative Return'] = (1 + MLM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MLMmean = MLM['returns'].mean()
MLMvar = MLM['returns'].var()
MLMkurt = MLM['returns'].kurt()
MLMskew = MLM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MLMbef = web.DataReader('MLM', 'yahoo', start, crashstart)
MLMbef['avgreturnbefore'] = MLMbef['Close'].pct_change(1)
MLMbef['Cumulative Return Before Crash'] = (1 + MLMbef['avgreturnbefore']).cumprod()

#period of crash
MLMdur = web.DataReader('MLM', 'yahoo', crashstart, crashend)
MLMdur['avgreturnduring'] = MLMdur['Close'].pct_change(1)
MLMdur['Cumulative Return During Crash'] = (1 + MLMdur['avgreturnduring']).cumprod()

#period after crash
MLMaft = web.DataReader('MLM', 'yahoo', crashend, end)
MLMaft['avgreturnafter'] = MLMaft['Close'].pct_change(1)
MLMaft['Cumulative Return After Crash'] = (1 + MLMaft['avgreturnafter']).cumprod()
In [59]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MOS = web.DataReader('MOS', 'yahoo', start, end)
MOS['returns'] = MOS['Close'].pct_change(1)
MOS['Cumulative Return'] = (1 + MOS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MOSmean = MOS['returns'].mean()
MOSvar = MOS['returns'].var()
MOSkurt = MOS['returns'].kurt()
MOSskew = MOS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MOSbef = web.DataReader('MOS', 'yahoo', start, crashstart)
MOSbef['avgreturnbefore'] = MOSbef['Close'].pct_change(1)
MOSbef['Cumulative Return Before Crash'] = (1 + MOSbef['avgreturnbefore']).cumprod()

#period of crash
MOSdur = web.DataReader('MOS', 'yahoo', crashstart, crashend)
MOSdur['avgreturnduring'] = MOSdur['Close'].pct_change(1)
MOSdur['Cumulative Return During Crash'] = (1 + MOSdur['avgreturnduring']).cumprod()

#period after crash
MOSaft = web.DataReader('MOS', 'yahoo', crashend, end)
MOSaft['avgreturnafter'] = MOSaft['Close'].pct_change(1)
MOSaft['Cumulative Return After Crash'] = (1 + MOSaft['avgreturnafter']).cumprod()
In [60]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NEM = web.DataReader('NEM', 'yahoo', start, end)
NEM['returns'] = NEM['Close'].pct_change(1)
NEM['Cumulative Return'] = (1 + NEM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NEMmean = NEM['returns'].mean()
NEMvar = NEM['returns'].var()
NEMkurt = NEM['returns'].kurt()
NEMskew = NEM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NEMbef = web.DataReader('NEM', 'yahoo', start, crashstart)
NEMbef['avgreturnbefore'] = NEMbef['Close'].pct_change(1)
NEMbef['Cumulative Return Before Crash'] = (1 + NEMbef['avgreturnbefore']).cumprod()

#period of crash
NEMdur = web.DataReader('NEM', 'yahoo', crashstart, crashend)
NEMdur['avgreturnduring'] = NEMdur['Close'].pct_change(1)
NEMdur['Cumulative Return During Crash'] = (1 + NEMdur['avgreturnduring']).cumprod()

#period after crash
NEMaft = web.DataReader('NEM', 'yahoo', crashend, end)
NEMaft['avgreturnafter'] = NEMaft['Close'].pct_change(1)
NEMaft['Cumulative Return After Crash'] = (1 + NEMaft['avgreturnafter']).cumprod()
In [61]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NUE = web.DataReader('NUE', 'yahoo', start, end)
NUE['returns'] = NUE['Close'].pct_change(1)
NUE['Cumulative Return'] = (1 + NUE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NUEmean = NUE['returns'].mean()
NUEvar = NUE['returns'].var()
NUEkurt = NUE['returns'].kurt()
NUEskew = NUE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NUEbef = web.DataReader('NUE', 'yahoo', start, crashstart)
NUEbef['avgreturnbefore'] = NUEbef['Close'].pct_change(1)
NUEbef['Cumulative Return Before Crash'] = (1 + NUEbef['avgreturnbefore']).cumprod()

#period of crash
NUEdur = web.DataReader('NUE', 'yahoo', crashstart, crashend)
NUEdur['avgreturnduring'] = NUEdur['Close'].pct_change(1)
NUEdur['Cumulative Return During Crash'] = (1 + NUEdur['avgreturnduring']).cumprod()

#period after crash
NUEaft = web.DataReader('NUE', 'yahoo', crashend, end)
NUEaft['avgreturnafter'] = NUEaft['Close'].pct_change(1)
NUEaft['Cumulative Return After Crash'] = (1 + NUEaft['avgreturnafter']).cumprod()
In [62]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PKG = web.DataReader('PKG', 'yahoo', start, end)
PKG['returns'] = PKG['Close'].pct_change(1)
PKG['Cumulative Return'] = (1 + PKG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PKGmean = PKG['returns'].mean()
PKGvar = PKG['returns'].var()
PKGkurt = PKG['returns'].kurt()
PKGskew = PKG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PKGbef = web.DataReader('PKG', 'yahoo', start, crashstart)
PKGbef['avgreturnbefore'] = PKGbef['Close'].pct_change(1)
PKGbef['Cumulative Return Before Crash'] = (1 + PKGbef['avgreturnbefore']).cumprod()

#period of crash
PKGdur = web.DataReader('PKG', 'yahoo', crashstart, crashend)
PKGdur['avgreturnduring'] = PKGdur['Close'].pct_change(1)
PKGdur['Cumulative Return During Crash'] = (1 + PKGdur['avgreturnduring']).cumprod()

#period after crash
PKGaft = web.DataReader('PKG', 'yahoo', crashend, end)
PKGaft['avgreturnafter'] = PKGaft['Close'].pct_change(1)
PKGaft['Cumulative Return After Crash'] = (1 + PKGaft['avgreturnafter']).cumprod()
In [63]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PPG = web.DataReader('PPG', 'yahoo', start, end)
PPG['returns'] = PPG['Close'].pct_change(1)
PPG['Cumulative Return'] = (1 + PPG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PPGmean = PPG['returns'].mean()
PPGvar = PPG['returns'].var()
PPGkurt = PPG['returns'].kurt()
PPGskew = PPG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PPGbef = web.DataReader('PPG', 'yahoo', start, crashstart)
PPGbef['avgreturnbefore'] = PPGbef['Close'].pct_change(1)
PPGbef['Cumulative Return Before Crash'] = (1 + PPGbef['avgreturnbefore']).cumprod()

#period of crash
PPGdur = web.DataReader('PPG', 'yahoo', crashstart, crashend)
PPGdur['avgreturnduring'] = PPGdur['Close'].pct_change(1)
PPGdur['Cumulative Return During Crash'] = (1 + PPGdur['avgreturnduring']).cumprod()

#period after crash
PPGaft = web.DataReader('PPG', 'yahoo', crashend, end)
PPGaft['avgreturnafter'] = PPGaft['Close'].pct_change(1)
PPGaft['Cumulative Return After Crash'] = (1 + PPGaft['avgreturnafter']).cumprod()
In [64]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SEE = web.DataReader('SEE', 'yahoo', start, end)
SEE['returns'] = SEE['Close'].pct_change(1)
SEE['Cumulative Return'] = (1 + SEE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SEEmean = SEE['returns'].mean()
SEEvar = SEE['returns'].var()
SEEkurt = SEE['returns'].kurt()
SEEskew = SEE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SEEbef = web.DataReader('SEE', 'yahoo', start, crashstart)
SEEbef['avgreturnbefore'] = SEEbef['Close'].pct_change(1)
SEEbef['Cumulative Return Before Crash'] = (1 + SEEbef['avgreturnbefore']).cumprod()

#period of crash
SEEdur = web.DataReader('SEE', 'yahoo', crashstart, crashend)
SEEdur['avgreturnduring'] = SEEdur['Close'].pct_change(1)
SEEdur['Cumulative Return During Crash'] = (1 + SEEdur['avgreturnduring']).cumprod()

#period after crash
SEEaft = web.DataReader('SEE', 'yahoo', crashend, end)
SEEaft['avgreturnafter'] = SEEaft['Close'].pct_change(1)
SEEaft['Cumulative Return After Crash'] = (1 + SEEaft['avgreturnafter']).cumprod()
In [65]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SHW = web.DataReader('SHW', 'yahoo', start, end)
SHW['returns'] = SHW['Close'].pct_change(1)
SHW['Cumulative Return'] = (1 + SHW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SHWmean = SHW['returns'].mean()
SHWvar = SHW['returns'].var()
SHWkurt = SHW['returns'].kurt()
SHWskew = SHW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SHWbef = web.DataReader('SHW', 'yahoo', start, crashstart)
SHWbef['avgreturnbefore'] = SHWbef['Close'].pct_change(1)
SHWbef['Cumulative Return Before Crash'] = (1 + SHWbef['avgreturnbefore']).cumprod()

#period of crash
SHWdur = web.DataReader('SHW', 'yahoo', crashstart, crashend)
SHWdur['avgreturnduring'] = SHWdur['Close'].pct_change(1)
SHWdur['Cumulative Return During Crash'] = (1 + SHWdur['avgreturnduring']).cumprod()

#period after crash
SHWaft = web.DataReader('SHW', 'yahoo', crashend, end)
SHWaft['avgreturnafter'] = SHWaft['Close'].pct_change(1)
SHWaft['Cumulative Return After Crash'] = (1 + SHWaft['avgreturnafter']).cumprod()
In [66]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
VMC = web.DataReader('VMC', 'yahoo', start, end)
VMC['returns'] = VMC['Close'].pct_change(1)
VMC['Cumulative Return'] = (1 + VMC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
VMCmean = VMC['returns'].mean()
VMCvar = VMC['returns'].var()
VMCkurt = VMC['returns'].kurt()
VMCskew = VMC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
VMCbef = web.DataReader('VMC', 'yahoo', start, crashstart)
VMCbef['avgreturnbefore'] = VMCbef['Close'].pct_change(1)
VMCbef['Cumulative Return Before Crash'] = (1 + VMCbef['avgreturnbefore']).cumprod()

#period of crash
VMCdur = web.DataReader('VMC', 'yahoo', crashstart, crashend)
VMCdur['avgreturnduring'] = VMCdur['Close'].pct_change(1)
VMCdur['Cumulative Return During Crash'] = (1 + VMCdur['avgreturnduring']).cumprod()

#period after crash
VMCaft = web.DataReader('VMC', 'yahoo', crashend, end)
VMCaft['avgreturnafter'] = VMCaft['Close'].pct_change(1)
VMCaft['Cumulative Return After Crash'] = (1 + VMCaft['avgreturnafter']).cumprod()
In [67]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WRK = web.DataReader('WRK', 'yahoo', start, end)
WRK['returns'] = WRK['Close'].pct_change(1)
WRK['Cumulative Return'] = (1 + WRK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WRKmean = WRK['returns'].mean()
WRKvar = WRK['returns'].var()
WRKkurt = WRK['returns'].kurt()
WRKskew = WRK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WRKbef = web.DataReader('WRK', 'yahoo', start, crashstart)
WRKbef['avgreturnbefore'] = WRKbef['Close'].pct_change(1)
WRKbef['Cumulative Return Before Crash'] = (1 + WRKbef['avgreturnbefore']).cumprod()

#period of crash
WRKdur = web.DataReader('WRK', 'yahoo', crashstart, crashend)
WRKdur['avgreturnduring'] = WRKdur['Close'].pct_change(1)
WRKdur['Cumulative Return During Crash'] = (1 + WRKdur['avgreturnduring']).cumprod()

#period after crash
WRKaft = web.DataReader('WRK', 'yahoo', crashend, end)
WRKaft['avgreturnafter'] = WRKaft['Close'].pct_change(1)
WRKaft['Cumulative Return After Crash'] = (1 + WRKaft['avgreturnafter']).cumprod()
In [68]:
#KDE Plot for Materials Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
APD['returns'].plot(kind = 'kde', label ='APD', figsize = (10,8), alpha=0.5, title = 'KDE of Materials Stocks')
ALB['returns'].plot(kind = 'kde', label ='ALB', alpha=0.5)
AMCR['returns'].plot(kind = 'kde', label ='AMCR', alpha=0.5)
AVY['returns'].plot(kind = 'kde', label ='AVY', alpha=0.5)
BLL['returns'].plot(kind = 'kde', label ='BLL', alpha=0.5)
CE['returns'].plot(kind = 'kde', label ='CE', alpha=0.5)
CF['returns'].plot(kind = 'kde', label ='CF', alpha=0.5)
CTVA['returns'].plot(kind = 'kde', label ='CTVA', alpha=0.5)
DOW['returns'].plot(kind = 'kde', label ='DOW', alpha=0.5)
DD['returns'].plot(kind = 'kde', label ='DD', alpha=0.5)
EMN['returns'].plot(kind = 'kde', label ='EMN', alpha=0.5)
ECL['returns'].plot(kind = 'kde', label ='ECL', alpha=0.5)
FMC['returns'].plot(kind = 'kde', label ='FMC', alpha=0.5)
FCX['returns'].plot(kind = 'kde', label ='FCX', alpha=0.5)
IP['returns'].plot(kind = 'kde', label ='IP', alpha=0.5)
IFF['returns'].plot(kind = 'kde', label ='IFF', alpha=0.5)
LIN['returns'].plot(kind = 'kde', label ='LIN', alpha=0.5)
LYB['returns'].plot(kind = 'kde', label ='LYB', alpha=0.5)
MLM['returns'].plot(kind = 'kde', label ='MLM', alpha=0.5)
MOS['returns'].plot(kind = 'kde', label ='MOS', alpha=0.5)
NEM['returns'].plot(kind = 'kde', label ='NEM', alpha=0.5)
NUE['returns'].plot(kind = 'kde', label ='NUE', alpha=0.5)
PKG['returns'].plot(kind = 'kde', label ='PKG', alpha=0.5)
PPG['returns'].plot(kind = 'kde', label ='PPG', alpha=0.5)
SEE['returns'].plot(kind = 'kde', label ='SEE', alpha=0.5)
SHW['returns'].plot(kind = 'kde', label ='SHW', alpha=0.5)
VMC['returns'].plot(kind = 'kde', label ='VMC', alpha=0.5)
WRK['returns'].plot(kind = 'kde', label ='WRK', alpha=0.5)
plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [69]:
#Boxplots comparing returns for Materials Sector
box_df = pd.concat([sp['returns'],APD['returns'],ALB['returns'],AMCR['returns'],AVY['returns'],BLL['returns'],CE['returns'],CF['returns'],CTVA['returns'],DOW['returns'],DD['returns'],EMN['returns'],ECL['returns'],FMC['returns'],FCX['returns'],IP['returns'],IFF['returns'],LIN['returns'],LYB['returns'],MLM['returns'],MOS['returns'],NEM['returns'],NUE['returns'],PKG['returns'],PPG['returns'],SEE['returns'],SHW['returns'],VMC['returns'],WRK['returns']], axis=1)
box_df.columns = ['S&P 500','APD','ALB','AMCR','AVY','BLL','CE','CF','CTVA','DOW','DD','EMN','ECL','FMC','FCX','IP','IFF','LIN','LYB','MLM','MOS','NEM','NUE','PKG','PPG','SEE','SHW','VMC','WRK']
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Materials Sector Returns')
Out[69]:
<matplotlib.axes._subplots.AxesSubplot at 0x13f9cd85e80>
In [70]:
#Cumulative Returns of Materials Stocks for Whole period

sp['Cumulative Return'].plot(label="S&P 500")
APD['Cumulative Return'].plot(label='APD', figsize=(16,8), title = 'Cumulative Return of Materials Stocks')
ALB['Cumulative Return'].plot(label="ALB")
AMCR['Cumulative Return'].plot(label="AMCR")
AVY['Cumulative Return'].plot(label="AVY")
BLL['Cumulative Return'].plot(label="BLL")
CE['Cumulative Return'].plot(label="CE")
CF['Cumulative Return'].plot(label="CF")
CTVA['Cumulative Return'].plot(label="CTVA")
DOW['Cumulative Return'].plot(label="DOW")
DD['Cumulative Return'].plot(label="DD")
EMN['Cumulative Return'].plot(label="EMN")
ECL['Cumulative Return'].plot(label="ECL")
FMC['Cumulative Return'].plot(label="FMC")
FCX['Cumulative Return'].plot(label="FCX")
IP['Cumulative Return'].plot(label="IP")
IFF['Cumulative Return'].plot(label="IFF")
LIN['Cumulative Return'].plot(label="LIN")
LYB['Cumulative Return'].plot(label="LYB")
MLM['Cumulative Return'].plot(label="MLM")
MOS['Cumulative Return'].plot(label="MOS")
NEM['Cumulative Return'].plot(label="NEM")
NUE['Cumulative Return'].plot(label="NUE")
PKG['Cumulative Return'].plot(label="PKG")
PPG['Cumulative Return'].plot(label="PPG")
SEE['Cumulative Return'].plot(label="SEE")
SHW['Cumulative Return'].plot(label="SHW")
VMC['Cumulative Return'].plot(label="VMC")
WRK['Cumulative Return'].plot(label="WRK")

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [71]:
#Sorting by Cumulative Return


cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],APD['Cumulative Return'].iloc[-1],ALB['Cumulative Return'].iloc[-1],AMCR['Cumulative Return'].iloc[-1],AVY['Cumulative Return'].iloc[-1],BLL['Cumulative Return'].iloc[-1],CE['Cumulative Return'].iloc[-1],CF['Cumulative Return'].iloc[-1],CTVA['Cumulative Return'].iloc[-1],DOW['Cumulative Return'].iloc[-1],DD['Cumulative Return'].iloc[-1],EMN['Cumulative Return'].iloc[-1],ECL['Cumulative Return'].iloc[-1],FMC['Cumulative Return'].iloc[-1],FCX['Cumulative Return'].iloc[-1],IP['Cumulative Return'].iloc[-1],IFF['Cumulative Return'].iloc[-1],LIN['Cumulative Return'].iloc[-1],LYB['Cumulative Return'].iloc[-1],MLM['Cumulative Return'].iloc[-1],MOS['Cumulative Return'].iloc[-1],NEM['Cumulative Return'].iloc[-1],NUE['Cumulative Return'].iloc[-1],PKG['Cumulative Return'].iloc[-1],PPG['Cumulative Return'].iloc[-1],SEE['Cumulative Return'].iloc[-1],SHW['Cumulative Return'].iloc[-1],VMC['Cumulative Return'].iloc[-1],WRK['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],APDbef['Cumulative Return Before Crash'].iloc[-1],ALBbef['Cumulative Return Before Crash'].iloc[-1],AMCRbef['Cumulative Return Before Crash'].iloc[-1],AVYbef['Cumulative Return Before Crash'].iloc[-1],BLLbef['Cumulative Return Before Crash'].iloc[-1],CEbef['Cumulative Return Before Crash'].iloc[-1],CFbef['Cumulative Return Before Crash'].iloc[-1],CTVAbef['Cumulative Return Before Crash'].iloc[-1],DOWbef['Cumulative Return Before Crash'].iloc[-1],DDbef['Cumulative Return Before Crash'].iloc[-1],EMNbef['Cumulative Return Before Crash'].iloc[-1],ECLbef['Cumulative Return Before Crash'].iloc[-1],FMCbef['Cumulative Return Before Crash'].iloc[-1],FCXbef['Cumulative Return Before Crash'].iloc[-1],IPbef['Cumulative Return Before Crash'].iloc[-1],IFFbef['Cumulative Return Before Crash'].iloc[-1],LINbef['Cumulative Return Before Crash'].iloc[-1],LYBbef['Cumulative Return Before Crash'].iloc[-1],MLMbef['Cumulative Return Before Crash'].iloc[-1],MOSbef['Cumulative Return Before Crash'].iloc[-1],NEMbef['Cumulative Return Before Crash'].iloc[-1],NUEbef['Cumulative Return Before Crash'].iloc[-1],PKGbef['Cumulative Return Before Crash'].iloc[-1],PPGbef['Cumulative Return Before Crash'].iloc[-1],SEEbef['Cumulative Return Before Crash'].iloc[-1],SHWbef['Cumulative Return Before Crash'].iloc[-1],VMCbef['Cumulative Return Before Crash'].iloc[-1],WRKbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],APDdur['Cumulative Return During Crash'].iloc[-1],ALBdur['Cumulative Return During Crash'].iloc[-1],AMCRdur['Cumulative Return During Crash'].iloc[-1],AVYdur['Cumulative Return During Crash'].iloc[-1],BLLdur['Cumulative Return During Crash'].iloc[-1],CEdur['Cumulative Return During Crash'].iloc[-1],CFdur['Cumulative Return During Crash'].iloc[-1],CTVAdur['Cumulative Return During Crash'].iloc[-1],DOWdur['Cumulative Return During Crash'].iloc[-1],DDdur['Cumulative Return During Crash'].iloc[-1],EMNdur['Cumulative Return During Crash'].iloc[-1],ECLdur['Cumulative Return During Crash'].iloc[-1],FMCdur['Cumulative Return During Crash'].iloc[-1],FCXdur['Cumulative Return During Crash'].iloc[-1],IPdur['Cumulative Return During Crash'].iloc[-1],IFFdur['Cumulative Return During Crash'].iloc[-1],LINdur['Cumulative Return During Crash'].iloc[-1],LYBdur['Cumulative Return During Crash'].iloc[-1],MLMdur['Cumulative Return During Crash'].iloc[-1],MOSdur['Cumulative Return During Crash'].iloc[-1],NEMdur['Cumulative Return During Crash'].iloc[-1],NUEdur['Cumulative Return During Crash'].iloc[-1],PKGdur['Cumulative Return During Crash'].iloc[-1],PPGdur['Cumulative Return During Crash'].iloc[-1],SEEdur['Cumulative Return During Crash'].iloc[-1],SHWdur['Cumulative Return During Crash'].iloc[-1],VMCdur['Cumulative Return During Crash'].iloc[-1],WRKdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],APDaft['Cumulative Return After Crash'].iloc[-1],ALBaft['Cumulative Return After Crash'].iloc[-1],AMCRaft['Cumulative Return After Crash'].iloc[-1],AVYaft['Cumulative Return After Crash'].iloc[-1],BLLaft['Cumulative Return After Crash'].iloc[-1],CEaft['Cumulative Return After Crash'].iloc[-1],CFaft['Cumulative Return After Crash'].iloc[-1],CTVAaft['Cumulative Return After Crash'].iloc[-1],DOWaft['Cumulative Return After Crash'].iloc[-1],DDaft['Cumulative Return After Crash'].iloc[-1],EMNaft['Cumulative Return After Crash'].iloc[-1],ECLaft['Cumulative Return After Crash'].iloc[-1],FMCaft['Cumulative Return After Crash'].iloc[-1],FCXaft['Cumulative Return After Crash'].iloc[-1],IPaft['Cumulative Return After Crash'].iloc[-1],IFFaft['Cumulative Return After Crash'].iloc[-1],LINaft['Cumulative Return After Crash'].iloc[-1],LYBaft['Cumulative Return After Crash'].iloc[-1],MLMaft['Cumulative Return After Crash'].iloc[-1],MOSaft['Cumulative Return After Crash'].iloc[-1],NEMaft['Cumulative Return After Crash'].iloc[-1],NUEaft['Cumulative Return After Crash'].iloc[-1],PKGaft['Cumulative Return After Crash'].iloc[-1],PPGaft['Cumulative Return After Crash'].iloc[-1],SEEaft['Cumulative Return After Crash'].iloc[-1],SHWaft['Cumulative Return After Crash'].iloc[-1],VMCaft['Cumulative Return After Crash'].iloc[-1],WRKaft['Cumulative Return After Crash'].iloc[-1]]}                         

matdf2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','APD','ALB','AMCR','AVY','BLL','CE','CF','CTVA','DOW','DD','EMN','ECL','FMC','FCX','IP','IFF','LIN','LYB','MLM','MOS','NEM','NUE','PKG','PPG','SEE','SHW','VMC','WRK'])

pd.DataFrame(matdf2)
Out[71]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
APD 1.085882 1.115674 0.691973 1.406552
ALB 1.339955 1.498887 0.539709 1.656385
AMCR 1.068762 1.008841 0.644596 1.643505
AVY 0.959465 1.064110 0.575784 1.565968
BLL 1.124411 1.165019 0.678068 1.423373
CE 0.800262 0.891819 0.571166 1.571061
CF 0.730847 0.881840 0.558257 1.484573
CTVA 1.233897 1.270934 0.694330 1.398266
DOW 0.841048 0.932670 0.545119 1.654251
DD 0.894376 0.837183 0.541579 1.972593
EMN 1.025327 0.988137 0.517874 2.003647
ECL 1.248423 1.121982 0.606951 1.833253
FMC 1.065009 1.117429 0.592668 1.608133
FCX 0.977498 1.087309 0.446192 2.014842
IP 0.849217 0.934508 0.616298 1.474499
IFF 0.984574 0.947650 0.710528 1.462240
LIN 1.091393 1.090999 0.678119 1.475200
LYB 0.818430 0.911128 0.485783 1.849099
MLM 0.836382 0.957519 0.558432 1.564183
MOS 0.767556 1.005444 0.499188 1.529284
NEM 1.399438 1.229236 0.866112 1.314450
NUE 0.814437 0.857066 0.593939 1.599930
PKG 0.994423 0.891347 0.769122 1.450538
PPG 0.882649 0.924555 0.607406 1.571724
SEE 0.934806 0.929750 0.633371 1.587438
SHW 1.024470 1.022352 0.679188 1.475397
VMC 0.877802 0.960257 0.604021 1.513411
WRK 0.813865 1.029710 0.611837 1.291818
In [72]:
matdf2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[72]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
FCX 0.977498 1.087309 0.446192 2.014842
EMN 1.025327 0.988137 0.517874 2.003647
DD 0.894376 0.837183 0.541579 1.972593
LYB 0.818430 0.911128 0.485783 1.849099
ECL 1.248423 1.121982 0.606951 1.833253
ALB 1.339955 1.498887 0.539709 1.656385
DOW 0.841048 0.932670 0.545119 1.654251
AMCR 1.068762 1.008841 0.644596 1.643505
FMC 1.065009 1.117429 0.592668 1.608133
NUE 0.814437 0.857066 0.593939 1.599930
SEE 0.934806 0.929750 0.633371 1.587438
PPG 0.882649 0.924555 0.607406 1.571724
CE 0.800262 0.891819 0.571166 1.571061
AVY 0.959465 1.064110 0.575784 1.565968
MLM 0.836382 0.957519 0.558432 1.564183
MOS 0.767556 1.005444 0.499188 1.529284
VMC 0.877802 0.960257 0.604021 1.513411
CF 0.730847 0.881840 0.558257 1.484573
SHW 1.024470 1.022352 0.679188 1.475397
LIN 1.091393 1.090999 0.678119 1.475200
IP 0.849217 0.934508 0.616298 1.474499
IFF 0.984574 0.947650 0.710528 1.462240
PKG 0.994423 0.891347 0.769122 1.450538
S&P 500 1.026077 1.083678 0.663281 1.427519
BLL 1.124411 1.165019 0.678068 1.423373
APD 1.085882 1.115674 0.691973 1.406552
CTVA 1.233897 1.270934 0.694330 1.398266
NEM 1.399438 1.229236 0.866112 1.314450
WRK 0.813865 1.029710 0.611837 1.291818
In [73]:
#Table of Key Statistics of Materials Stocks
import pandas as pd
import statistics as stats

matmeans = [APDmean,ALBmean,AMCRmean,AVYmean,BLLmean,CEmean,CFmean,CTVAmean,DOWmean,DDmean,EMNmean,ECLmean,FMCmean,FCXmean,IPmean,IFFmean,LINmean,LYBmean,MLMmean,MOSmean,NEMmean,NUEmean,PKGmean,PPGmean,SEEmean,SHWmean,VMCmean,WRKmean]
mataveragemean = stats.mean(matmeans)
matvariances = [APDvar,ALBvar,AMCRvar,AVYvar,BLLvar,CEvar,CFvar,CTVAvar,DOWvar,DDvar,EMNvar,ECLvar,FMCvar,FCXvar,IPvar,IFFvar,LINvar,LYBvar,MLMvar,MOSvar,NEMvar,NUEvar,PKGvar,PPGvar,SEEvar,SHWvar,VMCvar,WRKvar]
mataveragevar = stats.mean(matvariances)
matkurtoses = [APDkurt,ALBkurt,AMCRkurt,AVYkurt,BLLkurt,CEkurt,CFkurt,CTVAkurt,DOWkurt,DDkurt,EMNkurt,ECLkurt,FMCkurt,FCXkurt,IPkurt,IFFkurt,LINkurt,LYBkurt,MLMkurt,MOSkurt,NEMkurt,NUEkurt,PKGkurt,PPGkurt,SEEkurt,SHWkurt,VMCkurt,WRKkurt]
mataveragekurt = stats.mean(matkurtoses)
matskews = [APDskew,ALBskew,AMCRskew,AVYskew,BLLskew,CEskew,CFskew,CTVAskew,DOWskew,DDskew,EMNskew,ECLskew,FMCskew,FCXskew,IPskew,IFFskew,LINskew,LYBskew,MLMskew,MOSskew,NEMskew,NUEskew,PKGskew,PPGskew,SEEskew,SHWskew,VMCskew,WRKskew]
mataverageskew = stats.mean(matskews)

Materials = {'Mean': [[spmean],[mataveragemean],[APDmean], [ALBmean],[AMCRmean],[AVYmean],[BLLmean],[CEmean],[CFmean],[CTVAmean],[DOWmean],[DDmean],[EMNmean],[ECLmean],[FMCmean],[FCXmean],[IPmean],[IFFmean],[LINmean],[LYBmean],[MLMmean],[MOSmean],[NEMmean],[NUEmean],[PKGmean],[PPGmean],[SEEmean],[SHWmean],[VMCmean],[WRKmean]], 
        'Variance': [[spvar],[mataveragevar],[APDvar],[ALBvar],[AMCRvar],[AVYvar],[BLLvar],[CEvar],[CFvar],[CTVAvar],[DOWvar],[DDvar],[EMNvar],[ECLvar],[FMCvar],[FCXvar],[IPvar],[IFFvar],[LINvar],[LYBvar],[MLMvar],[MOSvar],[NEMvar],[NUEvar],[PKGvar],[PPGvar],[SEEvar],[SHWvar],[VMCvar],[WRKvar]], 
          'Kurtosis': [[spkurt],[mataveragekurt],[APDkurt],[ALBkurt],[AMCRkurt],[AVYkurt],[BLLkurt],[CEkurt],[CFkurt],[CTVAkurt],[DOWkurt],[DDkurt],[EMNkurt],[ECLkurt],[FMCkurt],[FCXkurt],[IPkurt],[IFFkurt],[LINkurt],[LYBkurt],[MLMkurt],[MOSkurt],[NEMkurt],[NUEkurt],[PKGkurt],[PPGkurt],[SEEkurt],[SHWkurt],[VMCkurt],[WRKkurt]],
          'Skew': [[spskew],[mataverageskew],[APDskew],[ALBskew],[AMCRskew],[AVYskew],[BLLskew],[CEskew],[CFskew],[CTVAskew],[DOWskew],[DDskew],[EMNskew],[ECLskew],[FMCskew],[FCXskew],[IPskew],[IFFskew],[LINskew],[LYBskew],[MLMskew],[MOSskew],[NEMskew],[NUEskew],[PKGskew],[PPGskew],[SEEskew],[SHWskew],[VMCskew],[WRKskew]],
        }

matdf = pd.DataFrame(Materials, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','APD','ALB','AMCR','AVY','BLL','CE','CF','CTVA','DOW','DD','EMN','ECL','FMC','FCX','IP','IFF','LIN','LYB','MLM','MOS','NEM','NUE','PKG','PPG','SEE','SHW','VMC','WRK']
)

pd.DataFrame(matdf)
Out[73]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [0.0006024611705163008] [0.0017262583228038184] [4.736414372650555] [0.03830609533180734]
APD [0.0012267716762935754] [0.0011532605837173305] [4.282934230407951] [0.06666064114866616]
ALB [0.003302983681959445] [0.0019520137578985016] [1.9854384684715667] [-0.30559913955715684]
AMCR [0.0012514409658292344] [0.0014458254190810689] [6.37671268271698] [-0.15431882577450887]
AVY [0.00039296417076348364] [0.0014863347288511762] [10.555396605192211] [1.1696079187109425]
BLL [0.0014053295777580214] [0.0009641916870978283] [4.213530507670294] [0.49016037797143924]
CE [-0.000844330054292883] [0.0018129354838013745] [2.944173704848164] [-0.7067538154644983]
CF [-0.0015237624806588503] [0.0019090813668441817] [3.5078317468970024] [-0.28924306011699535]
CTVA [0.0024563757113319424] [0.0015694097292749882] [2.7534629207100627] [-0.35135599486428964]
DOW [-0.00017421507071854438] [0.0023582312128164785] [5.7701318078673305] [-0.46064616658314095]
DD [-6.697003314106614e-05] [0.0016461228809523745] [2.1854519110252513] [0.04762683112053008]
EMN [0.0009455872630597749] [0.0014885766346334971] [2.810841353889818] [-0.3464443733312197]
ECL [0.002459461112970868] [0.0014313355468802843] [9.931333878580645] [0.9549340146959011]
FMC [0.001390312779883886] [0.0017654257407024832] [5.2260903975875275] [-0.40055149735890483]
FCX [0.0013289277451835595] [0.003080091604223493] [6.982730833162832] [0.6386018954491858]
IP [-0.0005112032207861157] [0.0015965282459622328] [3.260762231608597] [0.5090087182472764]
IFF [0.00043457005086521775] [0.0011207499071176576] [2.0903247993458747] [-0.07146835912495714]
LIN [0.0011495253286683206] [0.0009183237536964414] [3.568596045184492] [0.11161052109199408]
LYB [-0.00011603931723518509] [0.002872463364840981] [5.787616588520943] [-0.5924676524068091]
MLM [-0.0004985035878287885] [0.0018423088446996928] [1.8789130645139078] [-0.02313107177717566]
MOS [-0.00027290009879787474] [0.003570108515458439] [4.245700626380708] [-0.42753881120995835]
NEM [0.003244126200528225] [0.001171189778716175] [4.9007243707733315] [0.6193605341362399]
NUE [-0.000950465413726909] [0.0013541146264064992] [4.088368681504074] [-0.12073609126010129]
PKG [0.0005717955993508143] [0.0012597528133383112] [3.4565562245146673] [0.7211416850091938]
PPG [-0.0004180554069368009] [0.001140597005205139] [3.3922752065372794] [-0.3403603617664435]
SEE [0.00042915002614247165] [0.0019775361201875234] [9.676876129881467] [0.9363414293661649]
SHW [0.0008022932135509968] [0.0012104323993741728] [8.673824601531873] [-0.4583895934310208]
VMC [-0.0001862014675622187] [0.001693282825548982] [5.322867827878958] [-0.09380366399875212]
WRK [-0.0003600561779981781] [0.0025450084611796098] [2.750134987011723] [-0.04967541963099626]
In [74]:
matdf.sort_values("Mean", ascending=False)
Out[74]:
Mean Variance Kurtosis Skew
ALB [0.003302983681959445] [0.0019520137578985016] [1.9854384684715667] [-0.30559913955715684]
NEM [0.003244126200528225] [0.001171189778716175] [4.9007243707733315] [0.6193605341362399]
ECL [0.002459461112970868] [0.0014313355468802843] [9.931333878580645] [0.9549340146959011]
CTVA [0.0024563757113319424] [0.0015694097292749882] [2.7534629207100627] [-0.35135599486428964]
BLL [0.0014053295777580214] [0.0009641916870978283] [4.213530507670294] [0.49016037797143924]
FMC [0.001390312779883886] [0.0017654257407024832] [5.2260903975875275] [-0.40055149735890483]
FCX [0.0013289277451835595] [0.003080091604223493] [6.982730833162832] [0.6386018954491858]
AMCR [0.0012514409658292344] [0.0014458254190810689] [6.37671268271698] [-0.15431882577450887]
APD [0.0012267716762935754] [0.0011532605837173305] [4.282934230407951] [0.06666064114866616]
LIN [0.0011495253286683206] [0.0009183237536964414] [3.568596045184492] [0.11161052109199408]
EMN [0.0009455872630597749] [0.0014885766346334971] [2.810841353889818] [-0.3464443733312197]
SHW [0.0008022932135509968] [0.0012104323993741728] [8.673824601531873] [-0.4583895934310208]
Average [0.0006024611705163008] [0.0017262583228038184] [4.736414372650555] [0.03830609533180734]
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
PKG [0.0005717955993508143] [0.0012597528133383112] [3.4565562245146673] [0.7211416850091938]
IFF [0.00043457005086521775] [0.0011207499071176576] [2.0903247993458747] [-0.07146835912495714]
SEE [0.00042915002614247165] [0.0019775361201875234] [9.676876129881467] [0.9363414293661649]
AVY [0.00039296417076348364] [0.0014863347288511762] [10.555396605192211] [1.1696079187109425]
DD [-6.697003314106614e-05] [0.0016461228809523745] [2.1854519110252513] [0.04762683112053008]
LYB [-0.00011603931723518509] [0.002872463364840981] [5.787616588520943] [-0.5924676524068091]
DOW [-0.00017421507071854438] [0.0023582312128164785] [5.7701318078673305] [-0.46064616658314095]
VMC [-0.0001862014675622187] [0.001693282825548982] [5.322867827878958] [-0.09380366399875212]
MOS [-0.00027290009879787474] [0.003570108515458439] [4.245700626380708] [-0.42753881120995835]
WRK [-0.0003600561779981781] [0.0025450084611796098] [2.750134987011723] [-0.04967541963099626]
PPG [-0.0004180554069368009] [0.001140597005205139] [3.3922752065372794] [-0.3403603617664435]
MLM [-0.0004985035878287885] [0.0018423088446996928] [1.8789130645139078] [-0.02313107177717566]
IP [-0.0005112032207861157] [0.0015965282459622328] [3.260762231608597] [0.5090087182472764]
CE [-0.000844330054292883] [0.0018129354838013745] [2.944173704848164] [-0.7067538154644983]
NUE [-0.000950465413726909] [0.0013541146264064992] [4.088368681504074] [-0.12073609126010129]
CF [-0.0015237624806588503] [0.0019090813668441817] [3.5078317468970024] [-0.28924306011699535]

Industrials

In [75]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MMM = web.DataReader('MMM', 'yahoo', start, end)
MMM['returns'] = MMM['Close'].pct_change(1)
MMM['Cumulative Return'] = (1 + MMM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MMMmean = MMM['returns'].mean()
MMMvar = MMM['returns'].var()
MMMkurt = MMM['returns'].kurt()
MMMskew = MMM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MMMbef = web.DataReader('MMM', 'yahoo', start, crashstart)
MMMbef['avgreturnbefore'] = MMMbef['Close'].pct_change(1)
MMMbef['Cumulative Return Before Crash'] = (1 + MMMbef['avgreturnbefore']).cumprod()

#period of crash
MMMdur = web.DataReader('MMM', 'yahoo', crashstart, crashend)
MMMdur['avgreturnduring'] = MMMdur['Close'].pct_change(1)
MMMdur['Cumulative Return During Crash'] = (1 + MMMdur['avgreturnduring']).cumprod()

#period after crash
MMMaft = web.DataReader('MMM', 'yahoo', crashend, end)
MMMaft['avgreturnafter'] = MMMaft['Close'].pct_change(1)
MMMaft['Cumulative Return After Crash'] = (1 + MMMaft['avgreturnafter']).cumprod()
In [76]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ALK = web.DataReader('ALK', 'yahoo', start, end)
ALK['returns'] = ALK['Close'].pct_change(1)
ALK['Cumulative Return'] = (1 + ALK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ALKmean = ALK['returns'].mean()
ALKvar = ALK['returns'].var()
ALKkurt = ALK['returns'].kurt()
ALKskew = ALK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ALKbef = web.DataReader('ALK', 'yahoo', start, crashstart)
ALKbef['avgreturnbefore'] = ALKbef['Close'].pct_change(1)
ALKbef['Cumulative Return Before Crash'] = (1 + ALKbef['avgreturnbefore']).cumprod()

#period of crash
ALKdur = web.DataReader('ALK', 'yahoo', crashstart, crashend)
ALKdur['avgreturnduring'] = ALKdur['Close'].pct_change(1)
ALKdur['Cumulative Return During Crash'] = (1 + ALKdur['avgreturnduring']).cumprod()

#period after crash
ALKaft = web.DataReader('ALK', 'yahoo', crashend, end)
ALKaft['avgreturnafter'] = ALKaft['Close'].pct_change(1)
ALKaft['Cumulative Return After Crash'] = (1 + ALKaft['avgreturnafter']).cumprod()
In [77]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ALLE = web.DataReader('ALLE', 'yahoo', start, end)
ALLE['returns'] = ALLE['Close'].pct_change(1)
ALLE['Cumulative Return'] = (1 + ALLE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ALLEmean = ALLE['returns'].mean()
ALLEvar = ALLE['returns'].var()
ALLEkurt = ALLE['returns'].kurt()
ALLEskew = ALLE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ALLEbef = web.DataReader('ALLE', 'yahoo', start, crashstart)
ALLEbef['avgreturnbefore'] = ALLEbef['Close'].pct_change(1)
ALLEbef['Cumulative Return Before Crash'] = (1 + ALLEbef['avgreturnbefore']).cumprod()

#period of crash
ALLEdur = web.DataReader('ALLE', 'yahoo', crashstart, crashend)
ALLEdur['avgreturnduring'] = ALLEdur['Close'].pct_change(1)
ALLEdur['Cumulative Return During Crash'] = (1 + ALLEdur['avgreturnduring']).cumprod()

#period after crash
ALLEaft = web.DataReader('ALLE', 'yahoo', crashend, end)
ALLEaft['avgreturnafter'] = ALLEaft['Close'].pct_change(1)
ALLEaft['Cumulative Return After Crash'] = (1 + ALLEaft['avgreturnafter']).cumprod()
In [78]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AAL = web.DataReader('AAL', 'yahoo', start, end)
AAL['returns'] = AAL['Close'].pct_change(1)
AAL['Cumulative Return'] = (1 + AAL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AALmean = AAL['returns'].mean()
AALvar = AAL['returns'].var()
AALkurt = AAL['returns'].kurt()
AALskew = AAL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AALbef = web.DataReader('AAL', 'yahoo', start, crashstart)
AALbef['avgreturnbefore'] = AALbef['Close'].pct_change(1)
AALbef['Cumulative Return Before Crash'] = (1 + AALbef['avgreturnbefore']).cumprod()

#period of crash
AALdur = web.DataReader('AAL', 'yahoo', crashstart, crashend)
AALdur['avgreturnduring'] = AALdur['Close'].pct_change(1)
AALdur['Cumulative Return During Crash'] = (1 + AALdur['avgreturnduring']).cumprod()

#period after crash
AALaft = web.DataReader('AAL', 'yahoo', crashend, end)
AALaft['avgreturnafter'] = AALaft['Close'].pct_change(1)
AALaft['Cumulative Return After Crash'] = (1 + AALaft['avgreturnafter']).cumprod()
In [79]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AME = web.DataReader('AME', 'yahoo', start, end)
AME['returns'] = AME['Close'].pct_change(1)
AME['Cumulative Return'] = (1 + AME['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AMEmean = AME['returns'].mean()
AMEvar = AME['returns'].var()
AMEkurt = AME['returns'].kurt()
AMEskew = AME['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AMEbef = web.DataReader('AME', 'yahoo', start, crashstart)
AMEbef['avgreturnbefore'] = AMEbef['Close'].pct_change(1)
AMEbef['Cumulative Return Before Crash'] = (1 + AMEbef['avgreturnbefore']).cumprod()

#period of crash
AMEdur = web.DataReader('AME', 'yahoo', crashstart, crashend)
AMEdur['avgreturnduring'] = AMEdur['Close'].pct_change(1)
AMEdur['Cumulative Return During Crash'] = (1 + AMEdur['avgreturnduring']).cumprod()

#period after crash
AMEaft = web.DataReader('AME', 'yahoo', crashend, end)
AMEaft['avgreturnafter'] = AMEaft['Close'].pct_change(1)
AMEaft['Cumulative Return After Crash'] = (1 + AMEaft['avgreturnafter']).cumprod()
In [80]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AOS = web.DataReader('AOS', 'yahoo', start, end)
AOS['returns'] = AOS['Close'].pct_change(1)
AOS['Cumulative Return'] = (1 + AOS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AOSmean = AOS['returns'].mean()
AOSvar = AOS['returns'].var()
AOSkurt = AOS['returns'].kurt()
AOSskew = AOS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AOSbef = web.DataReader('AOS', 'yahoo', start, crashstart)
AOSbef['avgreturnbefore'] = AOSbef['Close'].pct_change(1)
AOSbef['Cumulative Return Before Crash'] = (1 + AOSbef['avgreturnbefore']).cumprod()

#period of crash
AOSdur = web.DataReader('AOS', 'yahoo', crashstart, crashend)
AOSdur['avgreturnduring'] = AOSdur['Close'].pct_change(1)
AOSdur['Cumulative Return During Crash'] = (1 + AOSdur['avgreturnduring']).cumprod()

#period after crash
AOSaft = web.DataReader('AOS', 'yahoo', crashend, end)
AOSaft['avgreturnafter'] = AOSaft['Close'].pct_change(1)
AOSaft['Cumulative Return After Crash'] = (1 + AOSaft['avgreturnafter']).cumprod()
In [81]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BA = web.DataReader('BA', 'yahoo', start, end)
BA['returns'] = BA['Close'].pct_change(1)
BA['Cumulative Return'] = (1 + BA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BAmean = BA['returns'].mean()
BAvar = BA['returns'].var()
BAkurt = BA['returns'].kurt()
BAskew = BA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BAbef = web.DataReader('BA', 'yahoo', start, crashstart)
BAbef['avgreturnbefore'] = BAbef['Close'].pct_change(1)
BAbef['Cumulative Return Before Crash'] = (1 + BAbef['avgreturnbefore']).cumprod()

#period of crash
BAdur = web.DataReader('BA', 'yahoo', crashstart, crashend)
BAdur['avgreturnduring'] = BAdur['Close'].pct_change(1)
BAdur['Cumulative Return During Crash'] = (1 + BAdur['avgreturnduring']).cumprod()

#period after crash
BAaft = web.DataReader('BA', 'yahoo', crashend, end)
BAaft['avgreturnafter'] = BAaft['Close'].pct_change(1)
BAaft['Cumulative Return After Crash'] = (1 + BAaft['avgreturnafter']).cumprod()
In [82]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CHRW = web.DataReader('CHRW', 'yahoo', start, end)
CHRW['returns'] = CHRW['Close'].pct_change(1)
CHRW['Cumulative Return'] = (1 + CHRW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CHRWmean = CHRW['returns'].mean()
CHRWvar = CHRW['returns'].var()
CHRWkurt = CHRW['returns'].kurt()
CHRWskew = CHRW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CHRWbef = web.DataReader('CHRW', 'yahoo', start, crashstart)
CHRWbef['avgreturnbefore'] = CHRWbef['Close'].pct_change(1)
CHRWbef['Cumulative Return Before Crash'] = (1 + CHRWbef['avgreturnbefore']).cumprod()

#period of crash
CHRWdur = web.DataReader('CHRW', 'yahoo', crashstart, crashend)
CHRWdur['avgreturnduring'] = CHRWdur['Close'].pct_change(1)
CHRWdur['Cumulative Return During Crash'] = (1 + CHRWdur['avgreturnduring']).cumprod()

#period after crash
CHRWaft = web.DataReader('CHRW', 'yahoo', crashend, end)
CHRWaft['avgreturnafter'] = CHRWaft['Close'].pct_change(1)
CHRWaft['Cumulative Return After Crash'] = (1 + CHRWaft['avgreturnafter']).cumprod()
In [ ]:
In [83]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CAT = web.DataReader('CAT', 'yahoo', start, end)
CAT['returns'] = CAT['Close'].pct_change(1)
CAT['Cumulative Return'] = (1 + CAT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CATmean = CAT['returns'].mean()
CATvar = CAT['returns'].var()
CATkurt = CAT['returns'].kurt()
CATskew = CAT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CATbef = web.DataReader('CAT', 'yahoo', start, crashstart)
CATbef['avgreturnbefore'] = CATbef['Close'].pct_change(1)
CATbef['Cumulative Return Before Crash'] = (1 + CATbef['avgreturnbefore']).cumprod()

#period of crash
CATdur = web.DataReader('CAT', 'yahoo', crashstart, crashend)
CATdur['avgreturnduring'] = CATdur['Close'].pct_change(1)
CATdur['Cumulative Return During Crash'] = (1 + CATdur['avgreturnduring']).cumprod()

#period after crash
CATaft = web.DataReader('CAT', 'yahoo', crashend, end)
CATaft['avgreturnafter'] = CATaft['Close'].pct_change(1)
CATaft['Cumulative Return After Crash'] = (1 + CATaft['avgreturnafter']).cumprod()
In [84]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CTAS = web.DataReader('CTAS', 'yahoo', start, end)
CTAS['returns'] = CTAS['Close'].pct_change(1)
CTAS['Cumulative Return'] = (1 + CTAS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CTASmean = CTAS['returns'].mean()
CTASvar = CTAS['returns'].var()
CTASkurt = CTAS['returns'].kurt()
CTASskew = CTAS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CTASbef = web.DataReader('CTAS', 'yahoo', start, crashstart)
CTASbef['avgreturnbefore'] = CTASbef['Close'].pct_change(1)
CTASbef['Cumulative Return Before Crash'] = (1 + CTASbef['avgreturnbefore']).cumprod()

#period of crash
CTASdur = web.DataReader('CTAS', 'yahoo', crashstart, crashend)
CTASdur['avgreturnduring'] = CTASdur['Close'].pct_change(1)
CTASdur['Cumulative Return During Crash'] = (1 + CTASdur['avgreturnduring']).cumprod()

#period after crash
CTASaft = web.DataReader('CTAS', 'yahoo', crashend, end)
CTASaft['avgreturnafter'] = CTASaft['Close'].pct_change(1)
CTASaft['Cumulative Return After Crash'] = (1 + CTASaft['avgreturnafter']).cumprod()
In [85]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CPRT = web.DataReader('CPRT', 'yahoo', start, end)
CPRT['returns'] = CPRT['Close'].pct_change(1)
CPRT['Cumulative Return'] = (1 + CPRT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CPRTmean = CPRT['returns'].mean()
CPRTvar = CPRT['returns'].var()
CPRTkurt = CPRT['returns'].kurt()
CPRTskew = CPRT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CPRTbef = web.DataReader('CPRT', 'yahoo', start, crashstart)
CPRTbef['avgreturnbefore'] = CPRTbef['Close'].pct_change(1)
CPRTbef['Cumulative Return Before Crash'] = (1 + CPRTbef['avgreturnbefore']).cumprod()

#period of crash
CPRTdur = web.DataReader('CPRT', 'yahoo', crashstart, crashend)
CPRTdur['avgreturnduring'] = CPRTdur['Close'].pct_change(1)
CPRTdur['Cumulative Return During Crash'] = (1 + CPRTdur['avgreturnduring']).cumprod()

#period after crash
CPRTaft = web.DataReader('CPRT', 'yahoo', crashend, end)
CPRTaft['avgreturnafter'] = CPRTaft['Close'].pct_change(1)
CPRTaft['Cumulative Return After Crash'] = (1 + CPRTaft['avgreturnafter']).cumprod()
In [86]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CSX = web.DataReader('CSX', 'yahoo', start, end)
CSX['returns'] = CSX['Close'].pct_change(1)
CSX['Cumulative Return'] = (1 + CSX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CSXmean = CSX['returns'].mean()
CSXvar = CSX['returns'].var()
CSXkurt = CSX['returns'].kurt()
CSXskew = CSX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CSXbef = web.DataReader('CSX', 'yahoo', start, crashstart)
CSXbef['avgreturnbefore'] = CSXbef['Close'].pct_change(1)
CSXbef['Cumulative Return Before Crash'] = (1 + CSXbef['avgreturnbefore']).cumprod()

#period of crash
CSXdur = web.DataReader('CSX', 'yahoo', crashstart, crashend)
CSXdur['avgreturnduring'] = CSXdur['Close'].pct_change(1)
CSXdur['Cumulative Return During Crash'] = (1 + CSXdur['avgreturnduring']).cumprod()

#period after crash
CSXaft = web.DataReader('CSX', 'yahoo', crashend, end)
CSXaft['avgreturnafter'] = CSXaft['Close'].pct_change(1)
CSXaft['Cumulative Return After Crash'] = (1 + CSXaft['avgreturnafter']).cumprod()
In [87]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CMI = web.DataReader('CMI', 'yahoo', start, end)
CMI['returns'] = CMI['Close'].pct_change(1)
CMI['Cumulative Return'] = (1 + CMI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CMImean = CMI['returns'].mean()
CMIvar = CMI['returns'].var()
CMIkurt = CMI['returns'].kurt()
CMIskew = CMI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CMIbef = web.DataReader('CMI', 'yahoo', start, crashstart)
CMIbef['avgreturnbefore'] = CMIbef['Close'].pct_change(1)
CMIbef['Cumulative Return Before Crash'] = (1 + CMIbef['avgreturnbefore']).cumprod()

#period of crash
CMIdur = web.DataReader('CMI', 'yahoo', crashstart, crashend)
CMIdur['avgreturnduring'] = CMIdur['Close'].pct_change(1)
CMIdur['Cumulative Return During Crash'] = (1 + CMIdur['avgreturnduring']).cumprod()

#period after crash
CMIaft = web.DataReader('CMI', 'yahoo', crashend, end)
CMIaft['avgreturnafter'] = CMIaft['Close'].pct_change(1)
CMIaft['Cumulative Return After Crash'] = (1 + CMIaft['avgreturnafter']).cumprod()
In [88]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DE = web.DataReader('DE', 'yahoo', start, end)
DE['returns'] = DE['Close'].pct_change(1)
DE['Cumulative Return'] = (1 + DE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DEmean = DE['returns'].mean()
DEvar = DE['returns'].var()
DEkurt = DE['returns'].kurt()
DEskew = DE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DEbef = web.DataReader('DE', 'yahoo', start, crashstart)
DEbef['avgreturnbefore'] = DEbef['Close'].pct_change(1)
DEbef['Cumulative Return Before Crash'] = (1 + DEbef['avgreturnbefore']).cumprod()

#period of crash
DEdur = web.DataReader('DE', 'yahoo', crashstart, crashend)
DEdur['avgreturnduring'] = DEdur['Close'].pct_change(1)
DEdur['Cumulative Return During Crash'] = (1 + DEdur['avgreturnduring']).cumprod()

#period after crash
DEaft = web.DataReader('DE', 'yahoo', crashend, end)
DEaft['avgreturnafter'] = DEaft['Close'].pct_change(1)
DEaft['Cumulative Return After Crash'] = (1 + DEaft['avgreturnafter']).cumprod()
In [89]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DAL = web.DataReader('DAL', 'yahoo', start, end)
DAL['returns'] = DAL['Close'].pct_change(1)
DAL['Cumulative Return'] = (1 + DAL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DALmean = DAL['returns'].mean()
DALvar = DAL['returns'].var()
DALkurt = DAL['returns'].kurt()
DALskew = DAL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DALbef = web.DataReader('DAL', 'yahoo', start, crashstart)
DALbef['avgreturnbefore'] = DALbef['Close'].pct_change(1)
DALbef['Cumulative Return Before Crash'] = (1 + DALbef['avgreturnbefore']).cumprod()

#period of crash
DALdur = web.DataReader('DAL', 'yahoo', crashstart, crashend)
DALdur['avgreturnduring'] = DALdur['Close'].pct_change(1)
DALdur['Cumulative Return During Crash'] = (1 + DALdur['avgreturnduring']).cumprod()

#period after crash
DALaft = web.DataReader('DAL', 'yahoo', crashend, end)
DALaft['avgreturnafter'] = DALaft['Close'].pct_change(1)
DALaft['Cumulative Return After Crash'] = (1 + DALaft['avgreturnafter']).cumprod()
In [90]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DOV = web.DataReader('DOV', 'yahoo', start, end)
DOV['returns'] = DOV['Close'].pct_change(1)
DOV['Cumulative Return'] = (1 + DOV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DOVmean = DOV['returns'].mean()
DOVvar = DOV['returns'].var()
DOVkurt = DOV['returns'].kurt()
DOVskew = DOV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DOVbef = web.DataReader('DOV', 'yahoo', start, crashstart)
DOVbef['avgreturnbefore'] = DOVbef['Close'].pct_change(1)
DOVbef['Cumulative Return Before Crash'] = (1 + DOVbef['avgreturnbefore']).cumprod()

#period of crash
DOVdur = web.DataReader('DOV', 'yahoo', crashstart, crashend)
DOVdur['avgreturnduring'] = DOVdur['Close'].pct_change(1)
DOVdur['Cumulative Return During Crash'] = (1 + DOVdur['avgreturnduring']).cumprod()

#period after crash
DOVaft = web.DataReader('DOV', 'yahoo', crashend, end)
DOVaft['avgreturnafter'] = DOVaft['Close'].pct_change(1)
DOVaft['Cumulative Return After Crash'] = (1 + DOVaft['avgreturnafter']).cumprod()
In [91]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ETN = web.DataReader('ETN', 'yahoo', start, end)
ETN['returns'] = ETN['Close'].pct_change(1)
ETN['Cumulative Return'] = (1 + ETN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ETNmean = ETN['returns'].mean()
ETNvar = ETN['returns'].var()
ETNkurt = ETN['returns'].kurt()
ETNskew = ETN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ETNbef = web.DataReader('ETN', 'yahoo', start, crashstart)
ETNbef['avgreturnbefore'] = ETNbef['Close'].pct_change(1)
ETNbef['Cumulative Return Before Crash'] = (1 + ETNbef['avgreturnbefore']).cumprod()

#period of crash
ETNdur = web.DataReader('ETN', 'yahoo', crashstart, crashend)
ETNdur['avgreturnduring'] = ETNdur['Close'].pct_change(1)
ETNdur['Cumulative Return During Crash'] = (1 + ETNdur['avgreturnduring']).cumprod()

#period after crash
ETNaft = web.DataReader('ETN', 'yahoo', crashend, end)
ETNaft['avgreturnafter'] = ETNaft['Close'].pct_change(1)
ETNaft['Cumulative Return After Crash'] = (1 + ETNaft['avgreturnafter']).cumprod()
In [92]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EMR = web.DataReader('EMR', 'yahoo', start, end)
EMR['returns'] = EMR['Close'].pct_change(1)
EMR['Cumulative Return'] = (1 + EMR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EMRmean = EMR['returns'].mean()
EMRvar = EMR['returns'].var()
EMRkurt = EMR['returns'].kurt()
EMRskew = EMR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EMRbef = web.DataReader('EMR', 'yahoo', start, crashstart)
EMRbef['avgreturnbefore'] = EMRbef['Close'].pct_change(1)
EMRbef['Cumulative Return Before Crash'] = (1 + EMRbef['avgreturnbefore']).cumprod()

#period of crash
EMRdur = web.DataReader('EMR', 'yahoo', crashstart, crashend)
EMRdur['avgreturnduring'] = EMRdur['Close'].pct_change(1)
EMRdur['Cumulative Return During Crash'] = (1 + EMRdur['avgreturnduring']).cumprod()

#period after crash
EMRaft = web.DataReader('EMR', 'yahoo', crashend, end)
EMRaft['avgreturnafter'] = EMRaft['Close'].pct_change(1)
EMRaft['Cumulative Return After Crash'] = (1 + EMRaft['avgreturnafter']).cumprod()
In [93]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EFX = web.DataReader('EFX', 'yahoo', start, end)
EFX['returns'] = EFX['Close'].pct_change(1)
EFX['Cumulative Return'] = (1 + EFX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EFXmean = EFX['returns'].mean()
EFXvar = EFX['returns'].var()
EFXkurt = EFX['returns'].kurt()
EFXskew = EFX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EFXbef = web.DataReader('EFX', 'yahoo', start, crashstart)
EFXbef['avgreturnbefore'] = EFXbef['Close'].pct_change(1)
EFXbef['Cumulative Return Before Crash'] = (1 + EFXbef['avgreturnbefore']).cumprod()

#period of crash
EFXdur = web.DataReader('EFX', 'yahoo', crashstart, crashend)
EFXdur['avgreturnduring'] = EFXdur['Close'].pct_change(1)
EFXdur['Cumulative Return During Crash'] = (1 + EFXdur['avgreturnduring']).cumprod()

#period after crash
EFXaft = web.DataReader('EFX', 'yahoo', crashend, end)
EFXaft['avgreturnafter'] = EFXaft['Close'].pct_change(1)
EFXaft['Cumulative Return After Crash'] = (1 + EFXaft['avgreturnafter']).cumprod()
In [94]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EXPD = web.DataReader('EXPD', 'yahoo', start, end)
EXPD['returns'] = EXPD['Close'].pct_change(1)
EXPD['Cumulative Return'] = (1 + EXPD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EXPDmean = EXPD['returns'].mean()
EXPDvar = EXPD['returns'].var()
EXPDkurt = EXPD['returns'].kurt()
EXPDskew = EXPD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EXPDbef = web.DataReader('EXPD', 'yahoo', start, crashstart)
EXPDbef['avgreturnbefore'] = EXPDbef['Close'].pct_change(1)
EXPDbef['Cumulative Return Before Crash'] = (1 + EXPDbef['avgreturnbefore']).cumprod()

#period of crash
EXPDdur = web.DataReader('EXPD', 'yahoo', crashstart, crashend)
EXPDdur['avgreturnduring'] = EXPDdur['Close'].pct_change(1)
EXPDdur['Cumulative Return During Crash'] = (1 + EXPDdur['avgreturnduring']).cumprod()

#period after crash
EXPDaft = web.DataReader('EXPD', 'yahoo', crashend, end)
EXPDaft['avgreturnafter'] = EXPDaft['Close'].pct_change(1)
EXPDaft['Cumulative Return After Crash'] = (1 + EXPDaft['avgreturnafter']).cumprod()
In [95]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FAST = web.DataReader('FAST', 'yahoo', start, end)
FAST['returns'] = FAST['Close'].pct_change(1)
FAST['Cumulative Return'] = (1 + FAST['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FASTmean = FAST['returns'].mean()
FASTvar = FAST['returns'].var()
FASTkurt = FAST['returns'].kurt()
FASTskew = FAST['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FASTbef = web.DataReader('FAST', 'yahoo', start, crashstart)
FASTbef['avgreturnbefore'] = FASTbef['Close'].pct_change(1)
FASTbef['Cumulative Return Before Crash'] = (1 + FASTbef['avgreturnbefore']).cumprod()

#period of crash
FASTdur = web.DataReader('FAST', 'yahoo', crashstart, crashend)
FASTdur['avgreturnduring'] = FASTdur['Close'].pct_change(1)
FASTdur['Cumulative Return During Crash'] = (1 + FASTdur['avgreturnduring']).cumprod()

#period after crash
FASTaft = web.DataReader('FAST', 'yahoo', crashend, end)
FASTaft['avgreturnafter'] = FASTaft['Close'].pct_change(1)
FASTaft['Cumulative Return After Crash'] = (1 + FASTaft['avgreturnafter']).cumprod()
In [96]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FDX = web.DataReader('FDX', 'yahoo', start, end)
FDX['returns'] = FDX['Close'].pct_change(1)
FDX['Cumulative Return'] = (1 + FDX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FDXmean = FDX['returns'].mean()
FDXvar = FDX['returns'].var()
FDXkurt = FDX['returns'].kurt()
FDXskew = FDX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FDXbef = web.DataReader('FDX', 'yahoo', start, crashstart)
FDXbef['avgreturnbefore'] = FDXbef['Close'].pct_change(1)
FDXbef['Cumulative Return Before Crash'] = (1 + FDXbef['avgreturnbefore']).cumprod()

#period of crash
FDXdur = web.DataReader('FDX', 'yahoo', crashstart, crashend)
FDXdur['avgreturnduring'] = FDXdur['Close'].pct_change(1)
FDXdur['Cumulative Return During Crash'] = (1 + FDXdur['avgreturnduring']).cumprod()

#period after crash
FDXaft = web.DataReader('FDX', 'yahoo', crashend, end)
FDXaft['avgreturnafter'] = FDXaft['Close'].pct_change(1)
FDXaft['Cumulative Return After Crash'] = (1 + FDXaft['avgreturnafter']).cumprod()
In [97]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FLS = web.DataReader('FLS', 'yahoo', start, end)
FLS['returns'] = FLS['Close'].pct_change(1)
FLS['Cumulative Return'] = (1 + FLS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FLSmean = FLS['returns'].mean()
FLSvar = FLS['returns'].var()
FLSkurt = FLS['returns'].kurt()
FLSskew = FLS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FLSbef = web.DataReader('FLS', 'yahoo', start, crashstart)
FLSbef['avgreturnbefore'] = FLSbef['Close'].pct_change(1)
FLSbef['Cumulative Return Before Crash'] = (1 + FLSbef['avgreturnbefore']).cumprod()

#period of crash
FLSdur = web.DataReader('FLS', 'yahoo', crashstart, crashend)
FLSdur['avgreturnduring'] = FLSdur['Close'].pct_change(1)
FLSdur['Cumulative Return During Crash'] = (1 + FLSdur['avgreturnduring']).cumprod()

#period after crash
FLSaft = web.DataReader('FLS', 'yahoo', crashend, end)
FLSaft['avgreturnafter'] = FLSaft['Close'].pct_change(1)
FLSaft['Cumulative Return After Crash'] = (1 + FLSaft['avgreturnafter']).cumprod()
In [98]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FTV = web.DataReader('FTV', 'yahoo', start, end)
FTV['returns'] = FTV['Close'].pct_change(1)
FTV['Cumulative Return'] = (1 + FTV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FTVmean = FTV['returns'].mean()
FTVvar = FTV['returns'].var()
FTVkurt = FTV['returns'].kurt()
FTVskew = FTV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FTVbef = web.DataReader('FTV', 'yahoo', start, crashstart)
FTVbef['avgreturnbefore'] = FTVbef['Close'].pct_change(1)
FTVbef['Cumulative Return Before Crash'] = (1 + FTVbef['avgreturnbefore']).cumprod()

#period of crash
FTVdur = web.DataReader('FTV', 'yahoo', crashstart, crashend)
FTVdur['avgreturnduring'] = FTVdur['Close'].pct_change(1)
FTVdur['Cumulative Return During Crash'] = (1 + FTVdur['avgreturnduring']).cumprod()

#period after crash
FTVaft = web.DataReader('FTV', 'yahoo', crashend, end)
FTVaft['avgreturnafter'] = FTVaft['Close'].pct_change(1)
FTVaft['Cumulative Return After Crash'] = (1 + FTVaft['avgreturnafter']).cumprod()
In [99]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FBHS = web.DataReader('FBHS', 'yahoo', start, end)
FBHS['returns'] = FBHS['Close'].pct_change(1)
FBHS['Cumulative Return'] = (1 + FBHS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FBHSmean = FBHS['returns'].mean()
FBHSvar = FBHS['returns'].var()
FBHSkurt = FBHS['returns'].kurt()
FBHSskew = FBHS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FBHSbef = web.DataReader('FBHS', 'yahoo', start, crashstart)
FBHSbef['avgreturnbefore'] = FBHSbef['Close'].pct_change(1)
FBHSbef['Cumulative Return Before Crash'] = (1 + FBHSbef['avgreturnbefore']).cumprod()

#period of crash
FBHSdur = web.DataReader('FBHS', 'yahoo', crashstart, crashend)
FBHSdur['avgreturnduring'] = FBHSdur['Close'].pct_change(1)
FBHSdur['Cumulative Return During Crash'] = (1 + FBHSdur['avgreturnduring']).cumprod()

#period after crash
FBHSaft = web.DataReader('FBHS', 'yahoo', crashend, end)
FBHSaft['avgreturnafter'] = FBHSaft['Close'].pct_change(1)
FBHSaft['Cumulative Return After Crash'] = (1 + FBHSaft['avgreturnafter']).cumprod()
In [100]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GD = web.DataReader('GD', 'yahoo', start, end)
GD['returns'] = GD['Close'].pct_change(1)
GD['Cumulative Return'] = (1 + GD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GDmean = GD['returns'].mean()
GDvar = GD['returns'].var()
GDkurt = GD['returns'].kurt()
GDskew = GD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GDbef = web.DataReader('GD', 'yahoo', start, crashstart)
GDbef['avgreturnbefore'] = GDbef['Close'].pct_change(1)
GDbef['Cumulative Return Before Crash'] = (1 + GDbef['avgreturnbefore']).cumprod()

#period of crash
GDdur = web.DataReader('GD', 'yahoo', crashstart, crashend)
GDdur['avgreturnduring'] = GDdur['Close'].pct_change(1)
GDdur['Cumulative Return During Crash'] = (1 + GDdur['avgreturnduring']).cumprod()

#period after crash
GDaft = web.DataReader('GD', 'yahoo', crashend, end)
GDaft['avgreturnafter'] = GDaft['Close'].pct_change(1)
GDaft['Cumulative Return After Crash'] = (1 + GDaft['avgreturnafter']).cumprod()
In [101]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GE = web.DataReader('GE', 'yahoo', start, end)
GE['returns'] = GE['Close'].pct_change(1)
GE['Cumulative Return'] = (1 + GE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GEmean = GE['returns'].mean()
GEvar = GE['returns'].var()
GEkurt = GE['returns'].kurt()
GEskew = GE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GEbef = web.DataReader('GE', 'yahoo', start, crashstart)
GEbef['avgreturnbefore'] = GEbef['Close'].pct_change(1)
GEbef['Cumulative Return Before Crash'] = (1 + GEbef['avgreturnbefore']).cumprod()

#period of crash
GEdur = web.DataReader('GE', 'yahoo', crashstart, crashend)
GEdur['avgreturnduring'] = GEdur['Close'].pct_change(1)
GEdur['Cumulative Return During Crash'] = (1 + GEdur['avgreturnduring']).cumprod()

#period after crash
GEaft = web.DataReader('GE', 'yahoo', crashend, end)
GEaft['avgreturnafter'] = GEaft['Close'].pct_change(1)
GEaft['Cumulative Return After Crash'] = (1 + GEaft['avgreturnafter']).cumprod()
In [102]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GWW = web.DataReader('GWW', 'yahoo', start, end)
GWW['returns'] = GWW['Close'].pct_change(1)
GWW['Cumulative Return'] = (1 + GWW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GWWmean = GWW['returns'].mean()
GWWvar = GWW['returns'].var()
GWWkurt = GWW['returns'].kurt()
GWWskew = GWW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GWWbef = web.DataReader('GWW', 'yahoo', start, crashstart)
GWWbef['avgreturnbefore'] = GWWbef['Close'].pct_change(1)
GWWbef['Cumulative Return Before Crash'] = (1 + GWWbef['avgreturnbefore']).cumprod()

#period of crash
GWWdur = web.DataReader('GWW', 'yahoo', crashstart, crashend)
GWWdur['avgreturnduring'] = GWWdur['Close'].pct_change(1)
GWWdur['Cumulative Return During Crash'] = (1 + GWWdur['avgreturnduring']).cumprod()

#period after crash
GWWaft = web.DataReader('GWW', 'yahoo', crashend, end)
GWWaft['avgreturnafter'] = GWWaft['Close'].pct_change(1)
GWWaft['Cumulative Return After Crash'] = (1 + GWWaft['avgreturnafter']).cumprod()
In [103]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HON = web.DataReader('HON', 'yahoo', start, end)
HON['returns'] = HON['Close'].pct_change(1)
HON['Cumulative Return'] = (1 + HON['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HONmean = HON['returns'].mean()
HONvar = HON['returns'].var()
HONkurt = HON['returns'].kurt()
HONskew = HON['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HONbef = web.DataReader('HON', 'yahoo', start, crashstart)
HONbef['avgreturnbefore'] = HONbef['Close'].pct_change(1)
HONbef['Cumulative Return Before Crash'] = (1 + HONbef['avgreturnbefore']).cumprod()

#period of crash
HONdur = web.DataReader('HON', 'yahoo', crashstart, crashend)
HONdur['avgreturnduring'] = HONdur['Close'].pct_change(1)
HONdur['Cumulative Return During Crash'] = (1 + HONdur['avgreturnduring']).cumprod()

#period after crash
HONaft = web.DataReader('HON', 'yahoo', crashend, end)
HONaft['avgreturnafter'] = HONaft['Close'].pct_change(1)
HONaft['Cumulative Return After Crash'] = (1 + HONaft['avgreturnafter']).cumprod()
In [104]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HWM = web.DataReader('HWM', 'yahoo', start, end)
HWM['returns'] = HWM['Close'].pct_change(1)
HWM['Cumulative Return'] = (1 + HWM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HWMmean = HWM['returns'].mean()
HWMvar = HWM['returns'].var()
HWMkurt = HWM['returns'].kurt()
HWMskew = HWM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HWMbef = web.DataReader('HWM', 'yahoo', start, crashstart)
HWMbef['avgreturnbefore'] = HWMbef['Close'].pct_change(1)
HWMbef['Cumulative Return Before Crash'] = (1 + HWMbef['avgreturnbefore']).cumprod()

#period of crash
HWMdur = web.DataReader('HWM', 'yahoo', crashstart, crashend)
HWMdur['avgreturnduring'] = HWMdur['Close'].pct_change(1)
HWMdur['Cumulative Return During Crash'] = (1 + HWMdur['avgreturnduring']).cumprod()

#period after crash
HWMaft = web.DataReader('HWM', 'yahoo', crashend, end)
HWMaft['avgreturnafter'] = HWMaft['Close'].pct_change(1)
HWMaft['Cumulative Return After Crash'] = (1 + HWMaft['avgreturnafter']).cumprod()
In [105]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HII = web.DataReader('HII', 'yahoo', start, end)
HII['returns'] = HII['Close'].pct_change(1)
HII['Cumulative Return'] = (1 + HII['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HIImean = HII['returns'].mean()
HIIvar = HII['returns'].var()
HIIkurt = HII['returns'].kurt()
HIIskew = HII['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HIIbef = web.DataReader('HII', 'yahoo', start, crashstart)
HIIbef['avgreturnbefore'] = HIIbef['Close'].pct_change(1)
HIIbef['Cumulative Return Before Crash'] = (1 + HIIbef['avgreturnbefore']).cumprod()

#period of crash
HIIdur = web.DataReader('HII', 'yahoo', crashstart, crashend)
HIIdur['avgreturnduring'] = HIIdur['Close'].pct_change(1)
HIIdur['Cumulative Return During Crash'] = (1 + HIIdur['avgreturnduring']).cumprod()

#period after crash
HIIaft = web.DataReader('HII', 'yahoo', crashend, end)
HIIaft['avgreturnafter'] = HIIaft['Close'].pct_change(1)
HIIaft['Cumulative Return After Crash'] = (1 + HIIaft['avgreturnafter']).cumprod()
In [106]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IEX = web.DataReader('IEX', 'yahoo', start, end)
IEX['returns'] = IEX['Close'].pct_change(1)
IEX['Cumulative Return'] = (1 + IEX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IEXmean = IEX['returns'].mean()
IEXvar = IEX['returns'].var()
IEXkurt = IEX['returns'].kurt()
IEXskew = IEX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IEXbef = web.DataReader('IEX', 'yahoo', start, crashstart)
IEXbef['avgreturnbefore'] = IEXbef['Close'].pct_change(1)
IEXbef['Cumulative Return Before Crash'] = (1 + IEXbef['avgreturnbefore']).cumprod()

#period of crash
IEXdur = web.DataReader('IEX', 'yahoo', crashstart, crashend)
IEXdur['avgreturnduring'] = IEXdur['Close'].pct_change(1)
IEXdur['Cumulative Return During Crash'] = (1 + IEXdur['avgreturnduring']).cumprod()

#period after crash
IEXaft = web.DataReader('IEX', 'yahoo', crashend, end)
IEXaft['avgreturnafter'] = IEXaft['Close'].pct_change(1)
IEXaft['Cumulative Return After Crash'] = (1 + IEXaft['avgreturnafter']).cumprod()
In [107]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
INFO = web.DataReader('INFO', 'yahoo', start, end)
INFO['returns'] = INFO['Close'].pct_change(1)
INFO['Cumulative Return'] = (1 + INFO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
INFOmean = INFO['returns'].mean()
INFOvar = INFO['returns'].var()
INFOkurt = INFO['returns'].kurt()
INFOskew = INFO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
INFObef = web.DataReader('INFO', 'yahoo', start, crashstart)
INFObef['avgreturnbefore'] = INFObef['Close'].pct_change(1)
INFObef['Cumulative Return Before Crash'] = (1 + INFObef['avgreturnbefore']).cumprod()

#period of crash
INFOdur = web.DataReader('INFO', 'yahoo', crashstart, crashend)
INFOdur['avgreturnduring'] = INFOdur['Close'].pct_change(1)
INFOdur['Cumulative Return During Crash'] = (1 + INFOdur['avgreturnduring']).cumprod()

#period after crash
INFOaft = web.DataReader('INFO', 'yahoo', crashend, end)
INFOaft['avgreturnafter'] = INFOaft['Close'].pct_change(1)
INFOaft['Cumulative Return After Crash'] = (1 + INFOaft['avgreturnafter']).cumprod()
In [108]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ITW = web.DataReader('ITW', 'yahoo', start, end)
ITW['returns'] = ITW['Close'].pct_change(1)
ITW['Cumulative Return'] = (1 + ITW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ITWmean = ITW['returns'].mean()
ITWvar = ITW['returns'].var()
ITWkurt = ITW['returns'].kurt()
ITWskew = ITW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ITWbef = web.DataReader('ITW', 'yahoo', start, crashstart)
ITWbef['avgreturnbefore'] = ITWbef['Close'].pct_change(1)
ITWbef['Cumulative Return Before Crash'] = (1 + ITWbef['avgreturnbefore']).cumprod()

#period of crash
ITWdur = web.DataReader('ITW', 'yahoo', crashstart, crashend)
ITWdur['avgreturnduring'] = ITWdur['Close'].pct_change(1)
ITWdur['Cumulative Return During Crash'] = (1 + ITWdur['avgreturnduring']).cumprod()

#period after crash
ITWaft = web.DataReader('ITW', 'yahoo', crashend, end)
ITWaft['avgreturnafter'] = ITWaft['Close'].pct_change(1)
ITWaft['Cumulative Return After Crash'] = (1 + ITWaft['avgreturnafter']).cumprod()
In [109]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IR = web.DataReader('IR', 'yahoo', start, end)
IR['returns'] = IR['Close'].pct_change(1)
IR['Cumulative Return'] = (1 + IR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IRmean = IR['returns'].mean()
IRvar = IR['returns'].var()
IRkurt = IR['returns'].kurt()
IRskew = IR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IRbef = web.DataReader('IR', 'yahoo', start, crashstart)
IRbef['avgreturnbefore'] = IRbef['Close'].pct_change(1)
IRbef['Cumulative Return Before Crash'] = (1 + IRbef['avgreturnbefore']).cumprod()

#period of crash
IRdur = web.DataReader('IR', 'yahoo', crashstart, crashend)
IRdur['avgreturnduring'] = IRdur['Close'].pct_change(1)
IRdur['Cumulative Return During Crash'] = (1 + IRdur['avgreturnduring']).cumprod()

#period after crash
IRaft = web.DataReader('IR', 'yahoo', crashend, end)
IRaft['avgreturnafter'] = IRaft['Close'].pct_change(1)
IRaft['Cumulative Return After Crash'] = (1 + IRaft['avgreturnafter']).cumprod()
In [110]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
J = web.DataReader('J', 'yahoo', start, end)
J['returns'] = J['Close'].pct_change(1)
J['Cumulative Return'] = (1 + J['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
Jmean = J['returns'].mean()
Jvar = J['returns'].var()
Jkurt = J['returns'].kurt()
Jskew = J['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
Jbef = web.DataReader('J', 'yahoo', start, crashstart)
Jbef['avgreturnbefore'] = Jbef['Close'].pct_change(1)
Jbef['Cumulative Return Before Crash'] = (1 + Jbef['avgreturnbefore']).cumprod()

#period of crash
Jdur = web.DataReader('J', 'yahoo', crashstart, crashend)
Jdur['avgreturnduring'] = Jdur['Close'].pct_change(1)
Jdur['Cumulative Return During Crash'] = (1 + Jdur['avgreturnduring']).cumprod()

#period after crash
Jaft = web.DataReader('J', 'yahoo', crashend, end)
Jaft['avgreturnafter'] = Jaft['Close'].pct_change(1)
Jaft['Cumulative Return After Crash'] = (1 + Jaft['avgreturnafter']).cumprod()
In [111]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
JBHT = web.DataReader('JBHT', 'yahoo', start, end)
JBHT['returns'] = JBHT['Close'].pct_change(1)
JBHT['Cumulative Return'] = (1 + JBHT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
JBHTmean = JBHT['returns'].mean()
JBHTvar = JBHT['returns'].var()
JBHTkurt = JBHT['returns'].kurt()
JBHTskew = JBHT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
JBHTbef = web.DataReader('JBHT', 'yahoo', start, crashstart)
JBHTbef['avgreturnbefore'] = JBHTbef['Close'].pct_change(1)
JBHTbef['Cumulative Return Before Crash'] = (1 + JBHTbef['avgreturnbefore']).cumprod()

#period of crash
JBHTdur = web.DataReader('JBHT', 'yahoo', crashstart, crashend)
JBHTdur['avgreturnduring'] = JBHTdur['Close'].pct_change(1)
JBHTdur['Cumulative Return During Crash'] = (1 + JBHTdur['avgreturnduring']).cumprod()

#period after crash
JBHTaft = web.DataReader('JBHT', 'yahoo', crashend, end)
JBHTaft['avgreturnafter'] = JBHTaft['Close'].pct_change(1)
JBHTaft['Cumulative Return After Crash'] = (1 + JBHTaft['avgreturnafter']).cumprod()
In [112]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
JCI = web.DataReader('JCI', 'yahoo', start, end)
JCI['returns'] = JCI['Close'].pct_change(1)
JCI['Cumulative Return'] = (1 + JCI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
JCImean = JCI['returns'].mean()
JCIvar = JCI['returns'].var()
JCIkurt = JCI['returns'].kurt()
JCIskew = JCI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
JCIbef = web.DataReader('JCI', 'yahoo', start, crashstart)
JCIbef['avgreturnbefore'] = JCIbef['Close'].pct_change(1)
JCIbef['Cumulative Return Before Crash'] = (1 + JCIbef['avgreturnbefore']).cumprod()

#period of crash
JCIdur = web.DataReader('JCI', 'yahoo', crashstart, crashend)
JCIdur['avgreturnduring'] = JCIdur['Close'].pct_change(1)
JCIdur['Cumulative Return During Crash'] = (1 + JCIdur['avgreturnduring']).cumprod()

#period after crash
JCIaft = web.DataReader('JCI', 'yahoo', crashend, end)
JCIaft['avgreturnafter'] = JCIaft['Close'].pct_change(1)
JCIaft['Cumulative Return After Crash'] = (1 + JCIaft['avgreturnafter']).cumprod()
In [113]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KSU = web.DataReader('KSU', 'yahoo', start, end)
KSU['returns'] = KSU['Close'].pct_change(1)
KSU['Cumulative Return'] = (1 + KSU['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KSUmean = KSU['returns'].mean()
KSUvar = KSU['returns'].var()
KSUkurt = KSU['returns'].kurt()
KSUskew = KSU['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KSUbef = web.DataReader('KSU', 'yahoo', start, crashstart)
KSUbef['avgreturnbefore'] = KSUbef['Close'].pct_change(1)
KSUbef['Cumulative Return Before Crash'] = (1 + KSUbef['avgreturnbefore']).cumprod()

#period of crash
KSUdur = web.DataReader('KSU', 'yahoo', crashstart, crashend)
KSUdur['avgreturnduring'] = KSUdur['Close'].pct_change(1)
KSUdur['Cumulative Return During Crash'] = (1 + KSUdur['avgreturnduring']).cumprod()

#period after crash
KSUaft = web.DataReader('KSU', 'yahoo', crashend, end)
KSUaft['avgreturnafter'] = KSUaft['Close'].pct_change(1)
KSUaft['Cumulative Return After Crash'] = (1 + KSUaft['avgreturnafter']).cumprod()
In [114]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LHX = web.DataReader('LHX', 'yahoo', start, end)
LHX['returns'] = LHX['Close'].pct_change(1)
LHX['Cumulative Return'] = (1 + LHX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LHXmean = LHX['returns'].mean()
LHXvar = LHX['returns'].var()
LHXkurt = LHX['returns'].kurt()
LHXskew = LHX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LHXbef = web.DataReader('LHX', 'yahoo', start, crashstart)
LHXbef['avgreturnbefore'] = LHXbef['Close'].pct_change(1)
LHXbef['Cumulative Return Before Crash'] = (1 + LHXbef['avgreturnbefore']).cumprod()

#period of crash
LHXdur = web.DataReader('LHX', 'yahoo', crashstart, crashend)
LHXdur['avgreturnduring'] = LHXdur['Close'].pct_change(1)
LHXdur['Cumulative Return During Crash'] = (1 + LHXdur['avgreturnduring']).cumprod()

#period after crash
LHXaft = web.DataReader('LHX', 'yahoo', crashend, end)
LHXaft['avgreturnafter'] = LHXaft['Close'].pct_change(1)
LHXaft['Cumulative Return After Crash'] = (1 + LHXaft['avgreturnafter']).cumprod()
In [115]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LMT = web.DataReader('LMT', 'yahoo', start, end)
LMT['returns'] = LMT['Close'].pct_change(1)
LMT['Cumulative Return'] = (1 + LMT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LMTmean = LMT['returns'].mean()
LMTvar = LMT['returns'].var()
LMTkurt = LMT['returns'].kurt()
LMTskew = LMT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LMTbef = web.DataReader('LMT', 'yahoo', start, crashstart)
LMTbef['avgreturnbefore'] = LMTbef['Close'].pct_change(1)
LMTbef['Cumulative Return Before Crash'] = (1 + LMTbef['avgreturnbefore']).cumprod()

#period of crash
LMTdur = web.DataReader('LMT', 'yahoo', crashstart, crashend)
LMTdur['avgreturnduring'] = LMTdur['Close'].pct_change(1)
LMTdur['Cumulative Return During Crash'] = (1 + LMTdur['avgreturnduring']).cumprod()

#period after crash
LMTaft = web.DataReader('LMT', 'yahoo', crashend, end)
LMTaft['avgreturnafter'] = LMTaft['Close'].pct_change(1)
LMTaft['Cumulative Return After Crash'] = (1 + LMTaft['avgreturnafter']).cumprod()
In [116]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MAS = web.DataReader('MAS', 'yahoo', start, end)
MAS['returns'] = MAS['Close'].pct_change(1)
MAS['Cumulative Return'] = (1 + MAS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MASmean = MAS['returns'].mean()
MASvar = MAS['returns'].var()
MASkurt = MAS['returns'].kurt()
MASskew = MAS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MASbef = web.DataReader('MAS', 'yahoo', start, crashstart)
MASbef['avgreturnbefore'] = MASbef['Close'].pct_change(1)
MASbef['Cumulative Return Before Crash'] = (1 + MASbef['avgreturnbefore']).cumprod()

#period of crash
MASdur = web.DataReader('MAS', 'yahoo', crashstart, crashend)
MASdur['avgreturnduring'] = MASdur['Close'].pct_change(1)
MASdur['Cumulative Return During Crash'] = (1 + MASdur['avgreturnduring']).cumprod()

#period after crash
MASaft = web.DataReader('MAS', 'yahoo', crashend, end)
MASaft['avgreturnafter'] = MASaft['Close'].pct_change(1)
MASaft['Cumulative Return After Crash'] = (1 + MASaft['avgreturnafter']).cumprod()
In [117]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NLSN = web.DataReader('NLSN', 'yahoo', start, end)
NLSN['returns'] = NLSN['Close'].pct_change(1)
NLSN['Cumulative Return'] = (1 + NLSN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NLSNmean = NLSN['returns'].mean()
NLSNvar = NLSN['returns'].var()
NLSNkurt = NLSN['returns'].kurt()
NLSNskew = NLSN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NLSNbef = web.DataReader('NLSN', 'yahoo', start, crashstart)
NLSNbef['avgreturnbefore'] = NLSNbef['Close'].pct_change(1)
NLSNbef['Cumulative Return Before Crash'] = (1 + NLSNbef['avgreturnbefore']).cumprod()

#period of crash
NLSNdur = web.DataReader('NLSN', 'yahoo', crashstart, crashend)
NLSNdur['avgreturnduring'] = NLSNdur['Close'].pct_change(1)
NLSNdur['Cumulative Return During Crash'] = (1 + NLSNdur['avgreturnduring']).cumprod()

#period after crash
NLSNaft = web.DataReader('NLSN', 'yahoo', crashend, end)
NLSNaft['avgreturnafter'] = NLSNaft['Close'].pct_change(1)
NLSNaft['Cumulative Return After Crash'] = (1 + NLSNaft['avgreturnafter']).cumprod()
In [118]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NSC = web.DataReader('NSC', 'yahoo', start, end)
NSC['returns'] = NSC['Close'].pct_change(1)
NSC['Cumulative Return'] = (1 + NSC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NSCmean = NSC['returns'].mean()
NSCvar = NSC['returns'].var()
NSCkurt = NSC['returns'].kurt()
NSCskew = NSC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NSCbef = web.DataReader('NSC', 'yahoo', start, crashstart)
NSCbef['avgreturnbefore'] = NSCbef['Close'].pct_change(1)
NSCbef['Cumulative Return Before Crash'] = (1 + NSCbef['avgreturnbefore']).cumprod()

#period of crash
NSCdur = web.DataReader('NSC', 'yahoo', crashstart, crashend)
NSCdur['avgreturnduring'] = NSCdur['Close'].pct_change(1)
NSCdur['Cumulative Return During Crash'] = (1 + NSCdur['avgreturnduring']).cumprod()

#period after crash
NSCaft = web.DataReader('NSC', 'yahoo', crashend, end)
NSCaft['avgreturnafter'] = NSCaft['Close'].pct_change(1)
NSCaft['Cumulative Return After Crash'] = (1 + NSCaft['avgreturnafter']).cumprod()
In [119]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NOC = web.DataReader('NOC', 'yahoo', start, end)
NOC['returns'] = NOC['Close'].pct_change(1)
NOC['Cumulative Return'] = (1 + NOC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NOCmean = NOC['returns'].mean()
NOCvar = NOC['returns'].var()
NOCkurt = NOC['returns'].kurt()
NOCskew = NOC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NOCbef = web.DataReader('NOC', 'yahoo', start, crashstart)
NOCbef['avgreturnbefore'] = NOCbef['Close'].pct_change(1)
NOCbef['Cumulative Return Before Crash'] = (1 + NOCbef['avgreturnbefore']).cumprod()

#period of crash
NOCdur = web.DataReader('NOC', 'yahoo', crashstart, crashend)
NOCdur['avgreturnduring'] = NOCdur['Close'].pct_change(1)
NOCdur['Cumulative Return During Crash'] = (1 + NOCdur['avgreturnduring']).cumprod()

#period after crash
NOCaft = web.DataReader('NOC', 'yahoo', crashend, end)
NOCaft['avgreturnafter'] = NOCaft['Close'].pct_change(1)
NOCaft['Cumulative Return After Crash'] = (1 + NOCaft['avgreturnafter']).cumprod()
In [120]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ODFL = web.DataReader('ODFL', 'yahoo', start, end)
ODFL['returns'] = ODFL['Close'].pct_change(1)
ODFL['Cumulative Return'] = (1 + ODFL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ODFLmean = ODFL['returns'].mean()
ODFLvar = ODFL['returns'].var()
ODFLkurt = ODFL['returns'].kurt()
ODFLskew = ODFL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ODFLbef = web.DataReader('ODFL', 'yahoo', start, crashstart)
ODFLbef['avgreturnbefore'] = ODFLbef['Close'].pct_change(1)
ODFLbef['Cumulative Return Before Crash'] = (1 + ODFLbef['avgreturnbefore']).cumprod()

#period of crash
ODFLdur = web.DataReader('ODFL', 'yahoo', crashstart, crashend)
ODFLdur['avgreturnduring'] = ODFLdur['Close'].pct_change(1)
ODFLdur['Cumulative Return During Crash'] = (1 + ODFLdur['avgreturnduring']).cumprod()

#period after crash
ODFLaft = web.DataReader('ODFL', 'yahoo', crashend, end)
ODFLaft['avgreturnafter'] = ODFLaft['Close'].pct_change(1)
ODFLaft['Cumulative Return After Crash'] = (1 + ODFLaft['avgreturnafter']).cumprod()
In [ ]:
In [121]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PCAR = web.DataReader('PCAR', 'yahoo', start, end)
PCAR['returns'] = PCAR['Close'].pct_change(1)
PCAR['Cumulative Return'] = (1 + PCAR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PCARmean = PCAR['returns'].mean()
PCARvar = PCAR['returns'].var()
PCARkurt = PCAR['returns'].kurt()
PCARskew = PCAR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PCARbef = web.DataReader('PCAR', 'yahoo', start, crashstart)
PCARbef['avgreturnbefore'] = PCARbef['Close'].pct_change(1)
PCARbef['Cumulative Return Before Crash'] = (1 + PCARbef['avgreturnbefore']).cumprod()

#period of crash
PCARdur = web.DataReader('PCAR', 'yahoo', crashstart, crashend)
PCARdur['avgreturnduring'] = PCARdur['Close'].pct_change(1)
PCARdur['Cumulative Return During Crash'] = (1 + PCARdur['avgreturnduring']).cumprod()

#period after crash
PCARaft = web.DataReader('PCAR', 'yahoo', crashend, end)
PCARaft['avgreturnafter'] = PCARaft['Close'].pct_change(1)
PCARaft['Cumulative Return After Crash'] = (1 + PCARaft['avgreturnafter']).cumprod()
In [122]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PH = web.DataReader('PH', 'yahoo', start, end)
PH['returns'] = PH['Close'].pct_change(1)
PH['Cumulative Return'] = (1 + PH['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PHmean = PH['returns'].mean()
PHvar = PH['returns'].var()
PHkurt = PH['returns'].kurt()
PHskew = PH['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PHbef = web.DataReader('PH', 'yahoo', start, crashstart)
PHbef['avgreturnbefore'] = PHbef['Close'].pct_change(1)
PHbef['Cumulative Return Before Crash'] = (1 + PHbef['avgreturnbefore']).cumprod()

#period of crash
PHdur = web.DataReader('PH', 'yahoo', crashstart, crashend)
PHdur['avgreturnduring'] = PHdur['Close'].pct_change(1)
PHdur['Cumulative Return During Crash'] = (1 + PHdur['avgreturnduring']).cumprod()

#period after crash
PHaft = web.DataReader('PH', 'yahoo', crashend, end)
PHaft['avgreturnafter'] = PHaft['Close'].pct_change(1)
PHaft['Cumulative Return After Crash'] = (1 + PHaft['avgreturnafter']).cumprod()
In [123]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PNR = web.DataReader('PNR', 'yahoo', start, end)
PNR['returns'] = PNR['Close'].pct_change(1)
PNR['Cumulative Return'] = (1 + PNR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PNRmean = PNR['returns'].mean()
PNRvar = PNR['returns'].var()
PNRkurt = PNR['returns'].kurt()
PNRskew = PNR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PNRbef = web.DataReader('PNR', 'yahoo', start, crashstart)
PNRbef['avgreturnbefore'] = PNRbef['Close'].pct_change(1)
PNRbef['Cumulative Return Before Crash'] = (1 + PNRbef['avgreturnbefore']).cumprod()

#period of crash
PNRdur = web.DataReader('PNR', 'yahoo', crashstart, crashend)
PNRdur['avgreturnduring'] = PNRdur['Close'].pct_change(1)
PNRdur['Cumulative Return During Crash'] = (1 + PNRdur['avgreturnduring']).cumprod()

#period after crash
PNRaft = web.DataReader('PNR', 'yahoo', crashend, end)
PNRaft['avgreturnafter'] = PNRaft['Close'].pct_change(1)
PNRaft['Cumulative Return After Crash'] = (1 + PNRaft['avgreturnafter']).cumprod()
In [124]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PWR = web.DataReader('PWR', 'yahoo', start, end)
PWR['returns'] = PWR['Close'].pct_change(1)
PWR['Cumulative Return'] = (1 + PWR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PWRmean = PWR['returns'].mean()
PWRvar = PWR['returns'].var()
PWRkurt = PWR['returns'].kurt()
PWRskew = PWR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PWRbef = web.DataReader('PWR', 'yahoo', start, crashstart)
PWRbef['avgreturnbefore'] = PWRbef['Close'].pct_change(1)
PWRbef['Cumulative Return Before Crash'] = (1 + PWRbef['avgreturnbefore']).cumprod()

#period of crash
PWRdur = web.DataReader('PWR', 'yahoo', crashstart, crashend)
PWRdur['avgreturnduring'] = PWRdur['Close'].pct_change(1)
PWRdur['Cumulative Return During Crash'] = (1 + PWRdur['avgreturnduring']).cumprod()

#period after crash
PWRaft = web.DataReader('PWR', 'yahoo', crashend, end)
PWRaft['avgreturnafter'] = PWRaft['Close'].pct_change(1)
PWRaft['Cumulative Return After Crash'] = (1 + PWRaft['avgreturnafter']).cumprod()
In [125]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
RTX = web.DataReader('RTX', 'yahoo', start, end)
RTX['returns'] = RTX['Close'].pct_change(1)
RTX['Cumulative Return'] = (1 + RTX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
RTXmean = RTX['returns'].mean()
RTXvar = RTX['returns'].var()
RTXkurt = RTX['returns'].kurt()
RTXskew = RTX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
RTXbef = web.DataReader('RTX', 'yahoo', start, crashstart)
RTXbef['avgreturnbefore'] = RTXbef['Close'].pct_change(1)
RTXbef['Cumulative Return Before Crash'] = (1 + RTXbef['avgreturnbefore']).cumprod()

#period of crash
RTXdur = web.DataReader('RTX', 'yahoo', crashstart, crashend)
RTXdur['avgreturnduring'] = RTXdur['Close'].pct_change(1)
RTXdur['Cumulative Return During Crash'] = (1 + RTXdur['avgreturnduring']).cumprod()

#period after crash
RTXaft = web.DataReader('RTX', 'yahoo', crashend, end)
RTXaft['avgreturnafter'] = RTXaft['Close'].pct_change(1)
RTXaft['Cumulative Return After Crash'] = (1 + RTXaft['avgreturnafter']).cumprod()
In [126]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
RSG = web.DataReader('RSG', 'yahoo', start, end)
RSG['returns'] = RSG['Close'].pct_change(1)
RSG['Cumulative Return'] = (1 + RSG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
RSGmean = RSG['returns'].mean()
RSGvar = RSG['returns'].var()
RSGkurt = RSG['returns'].kurt()
RSGskew = RSG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
RSGbef = web.DataReader('RSG', 'yahoo', start, crashstart)
RSGbef['avgreturnbefore'] = RSGbef['Close'].pct_change(1)
RSGbef['Cumulative Return Before Crash'] = (1 + RSGbef['avgreturnbefore']).cumprod()

#period of crash
RSGdur = web.DataReader('RSG', 'yahoo', crashstart, crashend)
RSGdur['avgreturnduring'] = RSGdur['Close'].pct_change(1)
RSGdur['Cumulative Return During Crash'] = (1 + RSGdur['avgreturnduring']).cumprod()

#period after crash
RSGaft = web.DataReader('RSG', 'yahoo', crashend, end)
RSGaft['avgreturnafter'] = RSGaft['Close'].pct_change(1)
RSGaft['Cumulative Return After Crash'] = (1 + RSGaft['avgreturnafter']).cumprod()
In [127]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
RHI = web.DataReader('RHI', 'yahoo', start, end)
RHI['returns'] = RHI['Close'].pct_change(1)
RHI['Cumulative Return'] = (1 + RHI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
RHImean = RHI['returns'].mean()
RHIvar = RHI['returns'].var()
RHIkurt = RHI['returns'].kurt()
RHIskew = RHI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
RHIbef = web.DataReader('RHI', 'yahoo', start, crashstart)
RHIbef['avgreturnbefore'] = RHIbef['Close'].pct_change(1)
RHIbef['Cumulative Return Before Crash'] = (1 + RHIbef['avgreturnbefore']).cumprod()

#period of crash
RHIdur = web.DataReader('RHI', 'yahoo', crashstart, crashend)
RHIdur['avgreturnduring'] = RHIdur['Close'].pct_change(1)
RHIdur['Cumulative Return During Crash'] = (1 + RHIdur['avgreturnduring']).cumprod()

#period after crash
RHIaft = web.DataReader('RHI', 'yahoo', crashend, end)
RHIaft['avgreturnafter'] = RHIaft['Close'].pct_change(1)
RHIaft['Cumulative Return After Crash'] = (1 + RHIaft['avgreturnafter']).cumprod()
In [128]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ROK = web.DataReader('ROK', 'yahoo', start, end)
ROK['returns'] = ROK['Close'].pct_change(1)
ROK['Cumulative Return'] = (1 + ROK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ROKmean = ROK['returns'].mean()
ROKvar = ROK['returns'].var()
ROKkurt = ROK['returns'].kurt()
ROKskew = ROK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ROKbef = web.DataReader('ROK', 'yahoo', start, crashstart)
ROKbef['avgreturnbefore'] = ROKbef['Close'].pct_change(1)
ROKbef['Cumulative Return Before Crash'] = (1 + ROKbef['avgreturnbefore']).cumprod()

#period of crash
ROKdur = web.DataReader('ROK', 'yahoo', crashstart, crashend)
ROKdur['avgreturnduring'] = ROKdur['Close'].pct_change(1)
ROKdur['Cumulative Return During Crash'] = (1 + ROKdur['avgreturnduring']).cumprod()

#period after crash
ROKaft = web.DataReader('ROK', 'yahoo', crashend, end)
ROKaft['avgreturnafter'] = ROKaft['Close'].pct_change(1)
ROKaft['Cumulative Return After Crash'] = (1 + ROKaft['avgreturnafter']).cumprod()
In [129]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ROL = web.DataReader('ROL', 'yahoo', start, end)
ROL['returns'] = ROL['Close'].pct_change(1)
ROL['Cumulative Return'] = (1 + ROL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ROLmean = ROL['returns'].mean()
ROLvar = ROL['returns'].var()
ROLkurt = ROL['returns'].kurt()
ROLskew = ROL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ROLbef = web.DataReader('ROL', 'yahoo', start, crashstart)
ROLbef['avgreturnbefore'] = ROLbef['Close'].pct_change(1)
ROLbef['Cumulative Return Before Crash'] = (1 + ROLbef['avgreturnbefore']).cumprod()

#period of crash
ROLdur = web.DataReader('ROL', 'yahoo', crashstart, crashend)
ROLdur['avgreturnduring'] = ROLdur['Close'].pct_change(1)
ROLdur['Cumulative Return During Crash'] = (1 + ROLdur['avgreturnduring']).cumprod()

#period after crash
ROLaft = web.DataReader('ROL', 'yahoo', crashend, end)
ROLaft['avgreturnafter'] = ROLaft['Close'].pct_change(1)
ROLaft['Cumulative Return After Crash'] = (1 + ROLaft['avgreturnafter']).cumprod()
In [130]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ROP = web.DataReader('ROP', 'yahoo', start, end)
ROP['returns'] = ROP['Close'].pct_change(1)
ROP['Cumulative Return'] = (1 + ROP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ROPmean = ROP['returns'].mean()
ROPvar = ROP['returns'].var()
ROPkurt = ROP['returns'].kurt()
ROPskew = ROP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ROPbef = web.DataReader('ROP', 'yahoo', start, crashstart)
ROPbef['avgreturnbefore'] = ROPbef['Close'].pct_change(1)
ROPbef['Cumulative Return Before Crash'] = (1 + ROPbef['avgreturnbefore']).cumprod()

#period of crash
ROPdur = web.DataReader('ROP', 'yahoo', crashstart, crashend)
ROPdur['avgreturnduring'] = ROPdur['Close'].pct_change(1)
ROPdur['Cumulative Return During Crash'] = (1 + ROPdur['avgreturnduring']).cumprod()

#period after crash
ROPaft = web.DataReader('ROP', 'yahoo', crashend, end)
ROPaft['avgreturnafter'] = ROPaft['Close'].pct_change(1)
ROPaft['Cumulative Return After Crash'] = (1 + ROPaft['avgreturnafter']).cumprod()
In [131]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SNA = web.DataReader('SNA', 'yahoo', start, end)
SNA['returns'] = SNA['Close'].pct_change(1)
SNA['Cumulative Return'] = (1 + SNA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SNAmean = SNA['returns'].mean()
SNAvar = SNA['returns'].var()
SNAkurt = SNA['returns'].kurt()
SNAskew = SNA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SNAbef = web.DataReader('SNA', 'yahoo', start, crashstart)
SNAbef['avgreturnbefore'] = SNAbef['Close'].pct_change(1)
SNAbef['Cumulative Return Before Crash'] = (1 + SNAbef['avgreturnbefore']).cumprod()

#period of crash
SNAdur = web.DataReader('SNA', 'yahoo', crashstart, crashend)
SNAdur['avgreturnduring'] = SNAdur['Close'].pct_change(1)
SNAdur['Cumulative Return During Crash'] = (1 + SNAdur['avgreturnduring']).cumprod()

#period after crash
SNAaft = web.DataReader('SNA', 'yahoo', crashend, end)
SNAaft['avgreturnafter'] = SNAaft['Close'].pct_change(1)
SNAaft['Cumulative Return After Crash'] = (1 + SNAaft['avgreturnafter']).cumprod()
In [132]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LUV = web.DataReader('LUV', 'yahoo', start, end)
LUV['returns'] = LUV['Close'].pct_change(1)
LUV['Cumulative Return'] = (1 + LUV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LUVmean = LUV['returns'].mean()
LUVvar = LUV['returns'].var()
LUVkurt = LUV['returns'].kurt()
LUVskew = LUV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LUVbef = web.DataReader('LUV', 'yahoo', start, crashstart)
LUVbef['avgreturnbefore'] = LUVbef['Close'].pct_change(1)
LUVbef['Cumulative Return Before Crash'] = (1 + LUVbef['avgreturnbefore']).cumprod()

#period of crash
LUVdur = web.DataReader('LUV', 'yahoo', crashstart, crashend)
LUVdur['avgreturnduring'] = LUVdur['Close'].pct_change(1)
LUVdur['Cumulative Return During Crash'] = (1 + LUVdur['avgreturnduring']).cumprod()

#period after crash
LUVaft = web.DataReader('LUV', 'yahoo', crashend, end)
LUVaft['avgreturnafter'] = LUVaft['Close'].pct_change(1)
LUVaft['Cumulative Return After Crash'] = (1 + LUVaft['avgreturnafter']).cumprod()
In [133]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SWK = web.DataReader('SWK', 'yahoo', start, end)
SWK['returns'] = SWK['Close'].pct_change(1)
SWK['Cumulative Return'] = (1 + SWK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SWKmean = SWK['returns'].mean()
SWKvar = SWK['returns'].var()
SWKkurt = SWK['returns'].kurt()
SWKskew = SWK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SWKbef = web.DataReader('SWK', 'yahoo', start, crashstart)
SWKbef['avgreturnbefore'] = SWKbef['Close'].pct_change(1)
SWKbef['Cumulative Return Before Crash'] = (1 + SWKbef['avgreturnbefore']).cumprod()

#period of crash
SWKdur = web.DataReader('SWK', 'yahoo', crashstart, crashend)
SWKdur['avgreturnduring'] = SWKdur['Close'].pct_change(1)
SWKdur['Cumulative Return During Crash'] = (1 + SWKdur['avgreturnduring']).cumprod()

#period after crash
SWKaft = web.DataReader('SWK', 'yahoo', crashend, end)
SWKaft['avgreturnafter'] = SWKaft['Close'].pct_change(1)
SWKaft['Cumulative Return After Crash'] = (1 + SWKaft['avgreturnafter']).cumprod()
In [134]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TXT = web.DataReader('TXT', 'yahoo', start, end)
TXT['returns'] = TXT['Close'].pct_change(1)
TXT['Cumulative Return'] = (1 + TXT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TXTmean = TXT['returns'].mean()
TXTvar = TXT['returns'].var()
TXTkurt = TXT['returns'].kurt()
TXTskew = TXT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TXTbef = web.DataReader('TXT', 'yahoo', start, crashstart)
TXTbef['avgreturnbefore'] = TXTbef['Close'].pct_change(1)
TXTbef['Cumulative Return Before Crash'] = (1 + TXTbef['avgreturnbefore']).cumprod()

#period of crash
TXTdur = web.DataReader('TXT', 'yahoo', crashstart, crashend)
TXTdur['avgreturnduring'] = TXTdur['Close'].pct_change(1)
TXTdur['Cumulative Return During Crash'] = (1 + TXTdur['avgreturnduring']).cumprod()

#period after crash
TXTaft = web.DataReader('TXT', 'yahoo', crashend, end)
TXTaft['avgreturnafter'] = TXTaft['Close'].pct_change(1)
TXTaft['Cumulative Return After Crash'] = (1 + TXTaft['avgreturnafter']).cumprod()
In [135]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TT = web.DataReader('TT', 'yahoo', start, end)
TT['returns'] = TT['Close'].pct_change(1)
TT['Cumulative Return'] = (1 + TT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TTmean = TT['returns'].mean()
TTvar = TT['returns'].var()
TTkurt = TT['returns'].kurt()
TTskew = TT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TTbef = web.DataReader('TT', 'yahoo', start, crashstart)
TTbef['avgreturnbefore'] = TTbef['Close'].pct_change(1)
TTbef['Cumulative Return Before Crash'] = (1 + TTbef['avgreturnbefore']).cumprod()

#period of crash
TTdur = web.DataReader('TT', 'yahoo', crashstart, crashend)
TTdur['avgreturnduring'] = TTdur['Close'].pct_change(1)
TTdur['Cumulative Return During Crash'] = (1 + TTdur['avgreturnduring']).cumprod()

#period after crash
TTaft = web.DataReader('TT', 'yahoo', crashend, end)
TTaft['avgreturnafter'] = TTaft['Close'].pct_change(1)
TTaft['Cumulative Return After Crash'] = (1 + TTaft['avgreturnafter']).cumprod()
In [136]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TDG = web.DataReader('TDG', 'yahoo', start, end)
TDG['returns'] = TDG['Close'].pct_change(1)
TDG['Cumulative Return'] = (1 + TDG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TDGmean = TDG['returns'].mean()
TDGvar = TDG['returns'].var()
TDGkurt = TDG['returns'].kurt()
TDGskew = TDG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TDGbef = web.DataReader('TDG', 'yahoo', start, crashstart)
TDGbef['avgreturnbefore'] = TDGbef['Close'].pct_change(1)
TDGbef['Cumulative Return Before Crash'] = (1 + TDGbef['avgreturnbefore']).cumprod()

#period of crash
TDGdur = web.DataReader('TDG', 'yahoo', crashstart, crashend)
TDGdur['avgreturnduring'] = TDGdur['Close'].pct_change(1)
TDGdur['Cumulative Return During Crash'] = (1 + TDGdur['avgreturnduring']).cumprod()

#period after crash
TDGaft = web.DataReader('TDG', 'yahoo', crashend, end)
TDGaft['avgreturnafter'] = TDGaft['Close'].pct_change(1)
TDGaft['Cumulative Return After Crash'] = (1 + TDGaft['avgreturnafter']).cumprod()
In [137]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
UNP = web.DataReader('UNP', 'yahoo', start, end)
UNP['returns'] = UNP['Close'].pct_change(1)
UNP['Cumulative Return'] = (1 + UNP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
UNPmean = UNP['returns'].mean()
UNPvar = UNP['returns'].var()
UNPkurt = UNP['returns'].kurt()
UNPskew = UNP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
UNPbef = web.DataReader('UNP', 'yahoo', start, crashstart)
UNPbef['avgreturnbefore'] = UNPbef['Close'].pct_change(1)
UNPbef['Cumulative Return Before Crash'] = (1 + UNPbef['avgreturnbefore']).cumprod()

#period of crash
UNPdur = web.DataReader('UNP', 'yahoo', crashstart, crashend)
UNPdur['avgreturnduring'] = UNPdur['Close'].pct_change(1)
UNPdur['Cumulative Return During Crash'] = (1 + UNPdur['avgreturnduring']).cumprod()

#period after crash
UNPaft = web.DataReader('UNP', 'yahoo', crashend, end)
UNPaft['avgreturnafter'] = UNPaft['Close'].pct_change(1)
UNPaft['Cumulative Return After Crash'] = (1 + UNPaft['avgreturnafter']).cumprod()
In [138]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
UAL = web.DataReader('UAL', 'yahoo', start, end)
UAL['returns'] = UAL['Close'].pct_change(1)
UAL['Cumulative Return'] = (1 + UAL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
UALmean = UAL['returns'].mean()
UALvar = UAL['returns'].var()
UALkurt = UAL['returns'].kurt()
UALskew = UAL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
UALbef = web.DataReader('UAL', 'yahoo', start, crashstart)
UALbef['avgreturnbefore'] = UALbef['Close'].pct_change(1)
UALbef['Cumulative Return Before Crash'] = (1 + UALbef['avgreturnbefore']).cumprod()

#period of crash
UALdur = web.DataReader('UAL', 'yahoo', crashstart, crashend)
UALdur['avgreturnduring'] = UALdur['Close'].pct_change(1)
UALdur['Cumulative Return During Crash'] = (1 + UALdur['avgreturnduring']).cumprod()

#period after crash
UALaft = web.DataReader('UAL', 'yahoo', crashend, end)
UALaft['avgreturnafter'] = UALaft['Close'].pct_change(1)
UALaft['Cumulative Return After Crash'] = (1 + UALaft['avgreturnafter']).cumprod()
In [139]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
UPS = web.DataReader('UPS', 'yahoo', start, end)
UPS['returns'] = UPS['Close'].pct_change(1)
UPS['Cumulative Return'] = (1 + UPS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
UPSmean = UPS['returns'].mean()
UPSvar = UPS['returns'].var()
UPSkurt = UPS['returns'].kurt()
UPSskew = UPS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
UPSbef = web.DataReader('UPS', 'yahoo', start, crashstart)
UPSbef['avgreturnbefore'] = UPSbef['Close'].pct_change(1)
UPSbef['Cumulative Return Before Crash'] = (1 + UPSbef['avgreturnbefore']).cumprod()

#period of crash
UPSdur = web.DataReader('UPS', 'yahoo', crashstart, crashend)
UPSdur['avgreturnduring'] = UPSdur['Close'].pct_change(1)
UPSdur['Cumulative Return During Crash'] = (1 + UPSdur['avgreturnduring']).cumprod()

#period after crash
UPSaft = web.DataReader('UPS', 'yahoo', crashend, end)
UPSaft['avgreturnafter'] = UPSaft['Close'].pct_change(1)
UPSaft['Cumulative Return After Crash'] = (1 + UPSaft['avgreturnafter']).cumprod()
In [140]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
URI = web.DataReader('URI', 'yahoo', start, end)
URI['returns'] = URI['Close'].pct_change(1)
URI['Cumulative Return'] = (1 + URI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
URImean = URI['returns'].mean()
URIvar = URI['returns'].var()
URIkurt = URI['returns'].kurt()
URIskew = URI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
URIbef = web.DataReader('URI', 'yahoo', start, crashstart)
URIbef['avgreturnbefore'] = URIbef['Close'].pct_change(1)
URIbef['Cumulative Return Before Crash'] = (1 + URIbef['avgreturnbefore']).cumprod()

#period of crash
URIdur = web.DataReader('URI', 'yahoo', crashstart, crashend)
URIdur['avgreturnduring'] = URIdur['Close'].pct_change(1)
URIdur['Cumulative Return During Crash'] = (1 + URIdur['avgreturnduring']).cumprod()

#period after crash
URIaft = web.DataReader('URI', 'yahoo', crashend, end)
URIaft['avgreturnafter'] = URIaft['Close'].pct_change(1)
URIaft['Cumulative Return After Crash'] = (1 + URIaft['avgreturnafter']).cumprod()
In [141]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
VRSK = web.DataReader('VRSK', 'yahoo', start, end)
VRSK['returns'] = VRSK['Close'].pct_change(1)
VRSK['Cumulative Return'] = (1 + VRSK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
VRSKmean = VRSK['returns'].mean()
VRSKvar = VRSK['returns'].var()
VRSKkurt = VRSK['returns'].kurt()
VRSKskew = VRSK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
VRSKbef = web.DataReader('VRSK', 'yahoo', start, crashstart)
VRSKbef['avgreturnbefore'] = VRSKbef['Close'].pct_change(1)
VRSKbef['Cumulative Return Before Crash'] = (1 + VRSKbef['avgreturnbefore']).cumprod()

#period of crash
VRSKdur = web.DataReader('VRSK', 'yahoo', crashstart, crashend)
VRSKdur['avgreturnduring'] = VRSKdur['Close'].pct_change(1)
VRSKdur['Cumulative Return During Crash'] = (1 + VRSKdur['avgreturnduring']).cumprod()

#period after crash
VRSKaft = web.DataReader('VRSK', 'yahoo', crashend, end)
VRSKaft['avgreturnafter'] = VRSKaft['Close'].pct_change(1)
VRSKaft['Cumulative Return After Crash'] = (1 + VRSKaft['avgreturnafter']).cumprod()
In [142]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WAB = web.DataReader('WAB', 'yahoo', start, end)
WAB['returns'] = WAB['Close'].pct_change(1)
WAB['Cumulative Return'] = (1 + WAB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WABmean = WAB['returns'].mean()
WABvar = WAB['returns'].var()
WABkurt = WAB['returns'].kurt()
WABskew = WAB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WABbef = web.DataReader('WAB', 'yahoo', start, crashstart)
WABbef['avgreturnbefore'] = WABbef['Close'].pct_change(1)
WABbef['Cumulative Return Before Crash'] = (1 + WABbef['avgreturnbefore']).cumprod()

#period of crash
WABdur = web.DataReader('WAB', 'yahoo', crashstart, crashend)
WABdur['avgreturnduring'] = WABdur['Close'].pct_change(1)
WABdur['Cumulative Return During Crash'] = (1 + WABdur['avgreturnduring']).cumprod()

#period after crash
WABaft = web.DataReader('WAB', 'yahoo', crashend, end)
WABaft['avgreturnafter'] = WABaft['Close'].pct_change(1)
WABaft['Cumulative Return After Crash'] = (1 + WABaft['avgreturnafter']).cumprod()
In [143]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WM = web.DataReader('WM', 'yahoo', start, end)
WM['returns'] = WM['Close'].pct_change(1)
WM['Cumulative Return'] = (1 + WM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WMmean = WM['returns'].mean()
WMvar = WM['returns'].var()
WMkurt = WM['returns'].kurt()
WMskew = WM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WMbef = web.DataReader('WM', 'yahoo', start, crashstart)
WMbef['avgreturnbefore'] = WMbef['Close'].pct_change(1)
WMbef['Cumulative Return Before Crash'] = (1 + WMbef['avgreturnbefore']).cumprod()

#period of crash
WMdur = web.DataReader('WM', 'yahoo', crashstart, crashend)
WMdur['avgreturnduring'] = WMdur['Close'].pct_change(1)
WMdur['Cumulative Return During Crash'] = (1 + WMdur['avgreturnduring']).cumprod()

#period after crash
WMaft = web.DataReader('WM', 'yahoo', crashend, end)
WMaft['avgreturnafter'] = WMaft['Close'].pct_change(1)
WMaft['Cumulative Return After Crash'] = (1 + WMaft['avgreturnafter']).cumprod()
In [144]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
XYL = web.DataReader('XYL', 'yahoo', start, end)
XYL['returns'] = XYL['Close'].pct_change(1)
XYL['Cumulative Return'] = (1 + XYL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
XYLmean = XYL['returns'].mean()
XYLvar = XYL['returns'].var()
XYLkurt = XYL['returns'].kurt()
XYLskew = XYL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
XYLbef = web.DataReader('XYL', 'yahoo', start, crashstart)
XYLbef['avgreturnbefore'] = XYLbef['Close'].pct_change(1)
XYLbef['Cumulative Return Before Crash'] = (1 + XYLbef['avgreturnbefore']).cumprod()

#period of crash
XYLdur = web.DataReader('XYL', 'yahoo', crashstart, crashend)
XYLdur['avgreturnduring'] = XYLdur['Close'].pct_change(1)
XYLdur['Cumulative Return During Crash'] = (1 + XYLdur['avgreturnduring']).cumprod()

#period after crash
XYLaft = web.DataReader('XYL', 'yahoo', crashend, end)
XYLaft['avgreturnafter'] = XYLaft['Close'].pct_change(1)
XYLaft['Cumulative Return After Crash'] = (1 + XYLaft['avgreturnafter']).cumprod()
In [145]:
#KDE Plot for Industrials Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
MMM['returns'].plot(kind = 'kde', label ='MMM', figsize = (10,8), alpha=0.5, title = 'KDE of Industrials Stocks')
ALK['returns'].plot(kind = 'kde', label ='ALK', alpha=0.5)
ALLE['returns'].plot(kind = 'kde', label ='ALLE', alpha=0.5)
AAL['returns'].plot(kind = 'kde', label ='AAL', alpha=0.5)
AME['returns'].plot(kind = 'kde', label ='AME', alpha=0.5)
AOS['returns'].plot(kind = 'kde', label ='AOS', alpha=0.5)
BA['returns'].plot(kind = 'kde', label ='BA', alpha=0.5)
CHRW['returns'].plot(kind = 'kde', label ='CHRW', alpha=0.5)
CAT['returns'].plot(kind = 'kde', label ='CAT', alpha=0.5)
CTAS['returns'].plot(kind = 'kde', label ='CTAS', alpha=0.5)
CPRT['returns'].plot(kind = 'kde', label ='CPRT', alpha=0.5)
CSX['returns'].plot(kind = 'kde', label ='CSX', alpha=0.5)
CMI['returns'].plot(kind = 'kde', label ='CMI', alpha=0.5)
DE['returns'].plot(kind = 'kde', label ='DE', alpha=0.5)
DAL['returns'].plot(kind = 'kde', label ='DAL', alpha=0.5)
DOV['returns'].plot(kind = 'kde', label ='DOV', alpha=0.5)
ETN['returns'].plot(kind = 'kde', label ='ETN', alpha=0.5)
EMR['returns'].plot(kind = 'kde', label ='EMR', alpha=0.5)
EFX['returns'].plot(kind = 'kde', label ='EFX', alpha=0.5)
EXPD['returns'].plot(kind = 'kde', label ='EXPD', alpha=0.5)
FAST['returns'].plot(kind = 'kde', label ='FAST', alpha=0.5)
FDX['returns'].plot(kind = 'kde', label ='FDX', alpha=0.5)
FLS['returns'].plot(kind = 'kde', label ='FLS', alpha=0.5)
FTV['returns'].plot(kind = 'kde', label ='FTV', alpha=0.5)
FBHS['returns'].plot(kind = 'kde', label ='FBHS', alpha=0.5)
GD['returns'].plot(kind = 'kde', label ='GD', alpha=0.5)
GE['returns'].plot(kind = 'kde', label ='GE', alpha=0.5)
GWW['returns'].plot(kind = 'kde', label ='GWW', alpha=0.5)
HON['returns'].plot(kind = 'kde', label ='HON', alpha=0.5)
HWM['returns'].plot(kind = 'kde', label ='HWM', alpha=0.5)
HII['returns'].plot(kind = 'kde', label ='HII', alpha=0.5)
IEX['returns'].plot(kind = 'kde', label ='IEX', alpha=0.5)
INFO['returns'].plot(kind = 'kde', label ='INFO', alpha=0.5)
ITW['returns'].plot(kind = 'kde', label ='ITW', alpha=0.5)
IR['returns'].plot(kind = 'kde', label ='IR', alpha=0.5)
J['returns'].plot(kind = 'kde', label ='J', alpha=0.5)
JBHT['returns'].plot(kind = 'kde', label ='JBHT', alpha=0.5)
JCI['returns'].plot(kind = 'kde', label ='JCI', alpha=0.5)
KSU['returns'].plot(kind = 'kde', label ='KSU', alpha=0.5)
LHX['returns'].plot(kind = 'kde', label ='LHX', alpha=0.5)
LMT['returns'].plot(kind = 'kde', label ='LMT', alpha=0.5)
MAS['returns'].plot(kind = 'kde', label ='MAS', alpha=0.5)
NLSN['returns'].plot(kind = 'kde', label ='NLSN', alpha=0.5)
NSC['returns'].plot(kind = 'kde', label ='NSC', alpha=0.5)
NOC['returns'].plot(kind = 'kde', label ='NOC', alpha=0.5)
ODFL['returns'].plot(kind = 'kde', label ='ODFL', alpha=0.5)
PCAR['returns'].plot(kind = 'kde', label ='PCAR', alpha=0.5)
PH['returns'].plot(kind = 'kde', label ='PH', alpha=0.5)
PNR['returns'].plot(kind = 'kde', label ='PNR', alpha=0.5)
PWR['returns'].plot(kind = 'kde', label ='PWR', alpha=0.5)
RTX['returns'].plot(kind = 'kde', label ='RTX', alpha=0.5)
RSG['returns'].plot(kind = 'kde', label ='RSG', alpha=0.5)
RHI['returns'].plot(kind = 'kde', label ='RHI', alpha=0.5)
ROK['returns'].plot(kind = 'kde', label ='ROK', alpha=0.5)
ROL['returns'].plot(kind = 'kde', label ='ROL', alpha=0.5)
ROP['returns'].plot(kind = 'kde', label ='ROP', alpha=0.5)
SNA['returns'].plot(kind = 'kde', label ='SNA', alpha=0.5)
LUV['returns'].plot(kind = 'kde', label ='LUV', alpha=0.5)
SWK['returns'].plot(kind = 'kde', label ='SWK', alpha=0.5)
TXT['returns'].plot(kind = 'kde', label ='TXT', alpha=0.5)
TT['returns'].plot(kind = 'kde', label ='TT', alpha=0.5)
TDG['returns'].plot(kind = 'kde', label ='TDG', alpha=0.5)
UNP['returns'].plot(kind = 'kde', label ='UNP', alpha=0.5)
UAL['returns'].plot(kind = 'kde', label ='UAL', alpha=0.5)
UPS['returns'].plot(kind = 'kde', label ='UPS', alpha=0.5)
URI['returns'].plot(kind = 'kde', label ='URI', alpha=0.5)
VRSK['returns'].plot(kind = 'kde', label ='VRSK', alpha=0.5)
WAB['returns'].plot(kind = 'kde', label ='WAB', alpha=0.5)
WM['returns'].plot(kind = 'kde', label ='WM', alpha=0.5)
XYL['returns'].plot(kind = 'kde', label ='XYL', alpha=0.5)
plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [146]:
#Boxplots comparing returns for Industrials Sector
box_df = pd.concat([sp['returns'],MMM['returns'],ALK['returns'],ALLE['returns'],AAL['returns'],AME['returns'],AOS['returns'],BA['returns'],CHRW['returns'],CAT['returns'],CTAS['returns'],CPRT['returns'],CSX['returns'],CMI['returns'],DE['returns'],DAL['returns'],DOV['returns'],ETN['returns'],EMR['returns'],EFX['returns'],EXPD['returns'],FAST['returns'],FDX['returns'],FLS['returns'],FTV['returns'],FBHS['returns'],GD['returns'],GE['returns'],GWW['returns'],HON['returns'],HWM['returns'],HII['returns'],IEX['returns'],INFO['returns'],ITW['returns'],IR['returns'],J['returns'],JBHT['returns'],JCI['returns'],KSU['returns'],LHX['returns'],LMT['returns'],MAS['returns'],NLSN['returns'],NSC['returns'],NOC['returns'],ODFL['returns'],PCAR['returns'],PH['returns'],PNR['returns'],PWR['returns'],RTX['returns'],RSG['returns'],RHI['returns'],ROK['returns'],ROL['returns'],ROP['returns'],SNA['returns'],LUV['returns'],SWK['returns'],TXT['returns'],TT['returns'],TDG['returns'],UNP['returns'],UAL['returns'],UPS['returns'],URI['returns'],VRSK['returns'],WAB['returns'],WM['returns'],XYL['returns']], axis=1)
box_df.columns = ['S&P 500','MMM','ALK','ALLE','AAL','AME','AOS','BA','CHRW','CAT','CTAS','CPRT','CSX','CMI','DE','DAL','DOV','ETN','EMR','EFX','EXPD','FAST','FDX','FLS','FTV','FBHS','GD','GE','GWW','HON','HWM','HII','IEX','INFO','ITW','IR','J','JBHT','JCI','KSU','LHX','LMT','MAS','NLSN','NSC','NOC','ODFL','PCAR','PH','PNR','PWR','RTX','RSG','RHI','ROK','ROL','ROP','SNA','LUV','SWK','TXT','TT','TDG','UNP','UAL','UPS','URI','VRSK','WAB','WM','XYL']
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Industrials Sector Returns')
Out[146]:
<matplotlib.axes._subplots.AxesSubplot at 0x13f9d715fd0>
In [147]:
#Cumulative Returns of Industrials Stocks for Whole period

sp['Cumulative Return'].plot(label="S&P 500")
MMM['Cumulative Return'].plot(label='MMM', figsize=(16,8), title = 'Cumulative Return of Industrials Stocks')
ALK['Cumulative Return'].plot(label="ALK")
ALLE['Cumulative Return'].plot(label="ALLE")
AAL['Cumulative Return'].plot(label="AAL")
AME['Cumulative Return'].plot(label="AME")
AOS['Cumulative Return'].plot(label="AOS")
BA['Cumulative Return'].plot(label="BA")
CHRW['Cumulative Return'].plot(label="CHRW")
CAT['Cumulative Return'].plot(label="CAT")
CTAS['Cumulative Return'].plot(label="CTAS")
CPRT['Cumulative Return'].plot(label="CPRT")
CSX['Cumulative Return'].plot(label="CSX")
CMI['Cumulative Return'].plot(label="CMI")
DE['Cumulative Return'].plot(label="DE")
DAL['Cumulative Return'].plot(label="DAL")
DOV['Cumulative Return'].plot(label="DOV")
ETN['Cumulative Return'].plot(label="ETN")
EMR['Cumulative Return'].plot(label="EMR")
EFX['Cumulative Return'].plot(label="EFX")
EXPD['Cumulative Return'].plot(label="EXPD")
FAST['Cumulative Return'].plot(label="FAST")
FDX['Cumulative Return'].plot(label="FDX")
FLS['Cumulative Return'].plot(label="FLS")
FTV['Cumulative Return'].plot(label="FTV")
FBHS['Cumulative Return'].plot(label="FBHS")
GD['Cumulative Return'].plot(label="GD")
GE['Cumulative Return'].plot(label="GE")
GWW['Cumulative Return'].plot(label="GWW")
HON['Cumulative Return'].plot(label="HON")
HWM['Cumulative Return'].plot(label="HWM")
HII['Cumulative Return'].plot(label="HII")
IEX['Cumulative Return'].plot(label="IEX")
INFO['Cumulative Return'].plot(label="INFO")
ITW['Cumulative Return'].plot(label="ITW")
IR['Cumulative Return'].plot(label="IR")
J['Cumulative Return'].plot(label="J")
JBHT['Cumulative Return'].plot(label="JBHT")
JCI['Cumulative Return'].plot(label="JCI")
KSU['Cumulative Return'].plot(label="KSU")
LHX['Cumulative Return'].plot(label="LHX")
LMT['Cumulative Return'].plot(label="LMT")
MAS['Cumulative Return'].plot(label="MAS")
NLSN['Cumulative Return'].plot(label="NLSN")
NSC['Cumulative Return'].plot(label="NSC")
NOC['Cumulative Return'].plot(label="NOC")
ODFL['Cumulative Return'].plot(label="ODFL")
PCAR['Cumulative Return'].plot(label="PCAR")
PH['Cumulative Return'].plot(label="PH")
PNR['Cumulative Return'].plot(label="PNR")
PWR['Cumulative Return'].plot(label="PWR")
RTX['Cumulative Return'].plot(label="RTX")
RSG['Cumulative Return'].plot(label="RSG")
RHI['Cumulative Return'].plot(label="RHI")
ROK['Cumulative Return'].plot(label="ROK")
ROL['Cumulative Return'].plot(label="ROL")
ROP['Cumulative Return'].plot(label="ROP")
SNA['Cumulative Return'].plot(label="SNA")
LUV['Cumulative Return'].plot(label="LUV")
SWK['Cumulative Return'].plot(label="SWK")
TXT['Cumulative Return'].plot(label="TXT")
TT['Cumulative Return'].plot(label="TT")
TDG['Cumulative Return'].plot(label="TDG")
UNP['Cumulative Return'].plot(label="UNP")
UAL['Cumulative Return'].plot(label="UAL")
UPS['Cumulative Return'].plot(label="UPS")
URI['Cumulative Return'].plot(label="URI")
VRSK['Cumulative Return'].plot(label="VRSK")
WAB['Cumulative Return'].plot(label="WAB")
WM['Cumulative Return'].plot(label="WM")
XYL['Cumulative Return'].plot(label="XYL")

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [148]:
#Sorting by Cumulative Return


cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],MMM['Cumulative Return'].iloc[-1],ALK['Cumulative Return'].iloc[-1],ALLE['Cumulative Return'].iloc[-1],AAL['Cumulative Return'].iloc[-1],AME['Cumulative Return'].iloc[-1],AOS['Cumulative Return'].iloc[-1],BA['Cumulative Return'].iloc[-1],CHRW['Cumulative Return'].iloc[-1],CAT['Cumulative Return'].iloc[-1],CTAS['Cumulative Return'].iloc[-1],CPRT['Cumulative Return'].iloc[-1],CSX['Cumulative Return'].iloc[-1],CMI['Cumulative Return'].iloc[-1],DE['Cumulative Return'].iloc[-1],DAL['Cumulative Return'].iloc[-1],DOV['Cumulative Return'].iloc[-1],ETN['Cumulative Return'].iloc[-1],EMR['Cumulative Return'].iloc[-1],EFX['Cumulative Return'].iloc[-1],EXPD['Cumulative Return'].iloc[-1],FAST['Cumulative Return'].iloc[-1],FDX['Cumulative Return'].iloc[-1],FLS['Cumulative Return'].iloc[-1],FTV['Cumulative Return'].iloc[-1],FBHS['Cumulative Return'].iloc[-1],GD['Cumulative Return'].iloc[-1],GE['Cumulative Return'].iloc[-1],GWW['Cumulative Return'].iloc[-1],HON['Cumulative Return'].iloc[-1],HWM['Cumulative Return'].iloc[-1],HII['Cumulative Return'].iloc[-1],IEX['Cumulative Return'].iloc[-1],INFO['Cumulative Return'].iloc[-1],ITW['Cumulative Return'].iloc[-1],IR['Cumulative Return'].iloc[-1],J['Cumulative Return'].iloc[-1],JBHT['Cumulative Return'].iloc[-1],JCI['Cumulative Return'].iloc[-1],KSU['Cumulative Return'].iloc[-1],LHX['Cumulative Return'].iloc[-1],LMT['Cumulative Return'].iloc[-1],MAS['Cumulative Return'].iloc[-1],NLSN['Cumulative Return'].iloc[-1],NSC['Cumulative Return'].iloc[-1],NOC['Cumulative Return'].iloc[-1],ODFL['Cumulative Return'].iloc[-1],PCAR['Cumulative Return'].iloc[-1],PH['Cumulative Return'].iloc[-1],PNR['Cumulative Return'].iloc[-1],PWR['Cumulative Return'].iloc[-1],RTX['Cumulative Return'].iloc[-1],RSG['Cumulative Return'].iloc[-1],RHI['Cumulative Return'].iloc[-1],ROK['Cumulative Return'].iloc[-1],ROL['Cumulative Return'].iloc[-1],ROP['Cumulative Return'].iloc[-1],SNA['Cumulative Return'].iloc[-1],LUV['Cumulative Return'].iloc[-1],SWK['Cumulative Return'].iloc[-1],TXT['Cumulative Return'].iloc[-1],TT['Cumulative Return'].iloc[-1],TDG['Cumulative Return'].iloc[-1],UNP['Cumulative Return'].iloc[-1],UAL['Cumulative Return'].iloc[-1],UPS['Cumulative Return'].iloc[-1],URI['Cumulative Return'].iloc[-1],VRSK['Cumulative Return'].iloc[-1],WAB['Cumulative Return'].iloc[-1],WM['Cumulative Return'].iloc[-1],XYL['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],MMMbef['Cumulative Return Before Crash'].iloc[-1],ALKbef['Cumulative Return Before Crash'].iloc[-1],ALLEbef['Cumulative Return Before Crash'].iloc[-1],AALbef['Cumulative Return Before Crash'].iloc[-1],AMEbef['Cumulative Return Before Crash'].iloc[-1],AOSbef['Cumulative Return Before Crash'].iloc[-1],BAbef['Cumulative Return Before Crash'].iloc[-1],CHRWbef['Cumulative Return Before Crash'].iloc[-1],CATbef['Cumulative Return Before Crash'].iloc[-1],CTASbef['Cumulative Return Before Crash'].iloc[-1],CPRTbef['Cumulative Return Before Crash'].iloc[-1],CSXbef['Cumulative Return Before Crash'].iloc[-1],CMIbef['Cumulative Return Before Crash'].iloc[-1],DEbef['Cumulative Return Before Crash'].iloc[-1],DALbef['Cumulative Return Before Crash'].iloc[-1],DOVbef['Cumulative Return Before Crash'].iloc[-1],ETNbef['Cumulative Return Before Crash'].iloc[-1],EMRbef['Cumulative Return Before Crash'].iloc[-1],EFXbef['Cumulative Return Before Crash'].iloc[-1],EXPDbef['Cumulative Return Before Crash'].iloc[-1],FASTbef['Cumulative Return Before Crash'].iloc[-1],FDXbef['Cumulative Return Before Crash'].iloc[-1],FLSbef['Cumulative Return Before Crash'].iloc[-1],FTVbef['Cumulative Return Before Crash'].iloc[-1],FBHSbef['Cumulative Return Before Crash'].iloc[-1],GDbef['Cumulative Return Before Crash'].iloc[-1],GEbef['Cumulative Return Before Crash'].iloc[-1],GWWbef['Cumulative Return Before Crash'].iloc[-1],HONbef['Cumulative Return Before Crash'].iloc[-1],HWMbef['Cumulative Return Before Crash'].iloc[-1],HIIbef['Cumulative Return Before Crash'].iloc[-1],IEXbef['Cumulative Return Before Crash'].iloc[-1],INFObef['Cumulative Return Before Crash'].iloc[-1],ITWbef['Cumulative Return Before Crash'].iloc[-1],IRbef['Cumulative Return Before Crash'].iloc[-1],Jbef['Cumulative Return Before Crash'].iloc[-1],JBHTbef['Cumulative Return Before Crash'].iloc[-1],JCIbef['Cumulative Return Before Crash'].iloc[-1],KSUbef['Cumulative Return Before Crash'].iloc[-1],LHXbef['Cumulative Return Before Crash'].iloc[-1],LMTbef['Cumulative Return Before Crash'].iloc[-1],MASbef['Cumulative Return Before Crash'].iloc[-1],NLSNbef['Cumulative Return Before Crash'].iloc[-1],NSCbef['Cumulative Return Before Crash'].iloc[-1],NOCbef['Cumulative Return Before Crash'].iloc[-1],ODFLbef['Cumulative Return Before Crash'].iloc[-1],PCARbef['Cumulative Return Before Crash'].iloc[-1],PHbef['Cumulative Return Before Crash'].iloc[-1],PNRbef['Cumulative Return Before Crash'].iloc[-1],PWRbef['Cumulative Return Before Crash'].iloc[-1],RTXbef['Cumulative Return Before Crash'].iloc[-1],RSGbef['Cumulative Return Before Crash'].iloc[-1],RHIbef['Cumulative Return Before Crash'].iloc[-1],ROKbef['Cumulative Return Before Crash'].iloc[-1],ROLbef['Cumulative Return Before Crash'].iloc[-1],ROPbef['Cumulative Return Before Crash'].iloc[-1],SNAbef['Cumulative Return Before Crash'].iloc[-1],LUVbef['Cumulative Return Before Crash'].iloc[-1],SWKbef['Cumulative Return Before Crash'].iloc[-1],TXTbef['Cumulative Return Before Crash'].iloc[-1],TTbef['Cumulative Return Before Crash'].iloc[-1],TDGbef['Cumulative Return Before Crash'].iloc[-1],UNPbef['Cumulative Return Before Crash'].iloc[-1],UALbef['Cumulative Return Before Crash'].iloc[-1],UPSbef['Cumulative Return Before Crash'].iloc[-1],URIbef['Cumulative Return Before Crash'].iloc[-1],VRSKbef['Cumulative Return Before Crash'].iloc[-1],WABbef['Cumulative Return Before Crash'].iloc[-1],WMbef['Cumulative Return Before Crash'].iloc[-1],XYLbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],MMMdur['Cumulative Return During Crash'].iloc[-1],ALKdur['Cumulative Return During Crash'].iloc[-1],ALLEdur['Cumulative Return During Crash'].iloc[-1],AALdur['Cumulative Return During Crash'].iloc[-1],AMEdur['Cumulative Return During Crash'].iloc[-1],AOSdur['Cumulative Return During Crash'].iloc[-1],BAdur['Cumulative Return During Crash'].iloc[-1],CHRWdur['Cumulative Return During Crash'].iloc[-1],CATdur['Cumulative Return During Crash'].iloc[-1],CTASdur['Cumulative Return During Crash'].iloc[-1],CPRTdur['Cumulative Return During Crash'].iloc[-1],CSXdur['Cumulative Return During Crash'].iloc[-1],CMIdur['Cumulative Return During Crash'].iloc[-1],DEdur['Cumulative Return During Crash'].iloc[-1],DALdur['Cumulative Return During Crash'].iloc[-1],DOVdur['Cumulative Return During Crash'].iloc[-1],ETNdur['Cumulative Return During Crash'].iloc[-1],EMRdur['Cumulative Return During Crash'].iloc[-1],EFXdur['Cumulative Return During Crash'].iloc[-1],EXPDdur['Cumulative Return During Crash'].iloc[-1],FASTdur['Cumulative Return During Crash'].iloc[-1],FDXdur['Cumulative Return During Crash'].iloc[-1],FLSdur['Cumulative Return During Crash'].iloc[-1],FTVdur['Cumulative Return During Crash'].iloc[-1],FBHSdur['Cumulative Return During Crash'].iloc[-1],GDdur['Cumulative Return During Crash'].iloc[-1],GEdur['Cumulative Return During Crash'].iloc[-1],GWWdur['Cumulative Return During Crash'].iloc[-1],HONdur['Cumulative Return During Crash'].iloc[-1],HWMdur['Cumulative Return During Crash'].iloc[-1],HIIdur['Cumulative Return During Crash'].iloc[-1],IEXdur['Cumulative Return During Crash'].iloc[-1],INFOdur['Cumulative Return During Crash'].iloc[-1],ITWdur['Cumulative Return During Crash'].iloc[-1],IRdur['Cumulative Return During Crash'].iloc[-1],Jdur['Cumulative Return During Crash'].iloc[-1],JBHTdur['Cumulative Return During Crash'].iloc[-1],JCIdur['Cumulative Return During Crash'].iloc[-1],KSUdur['Cumulative Return During Crash'].iloc[-1],LHXdur['Cumulative Return During Crash'].iloc[-1],LMTdur['Cumulative Return During Crash'].iloc[-1],MASdur['Cumulative Return During Crash'].iloc[-1],NLSNdur['Cumulative Return During Crash'].iloc[-1],NSCdur['Cumulative Return During Crash'].iloc[-1],NOCdur['Cumulative Return During Crash'].iloc[-1],ODFLdur['Cumulative Return During Crash'].iloc[-1],PCARdur['Cumulative Return During Crash'].iloc[-1],PHdur['Cumulative Return During Crash'].iloc[-1],PNRdur['Cumulative Return During Crash'].iloc[-1],PWRdur['Cumulative Return During Crash'].iloc[-1],RTXdur['Cumulative Return During Crash'].iloc[-1],RSGdur['Cumulative Return During Crash'].iloc[-1],RHIdur['Cumulative Return During Crash'].iloc[-1],ROKdur['Cumulative Return During Crash'].iloc[-1],ROLdur['Cumulative Return During Crash'].iloc[-1],ROPdur['Cumulative Return During Crash'].iloc[-1],SNAdur['Cumulative Return During Crash'].iloc[-1],LUVdur['Cumulative Return During Crash'].iloc[-1],SWKdur['Cumulative Return During Crash'].iloc[-1],TXTdur['Cumulative Return During Crash'].iloc[-1],TTdur['Cumulative Return During Crash'].iloc[-1],TDGdur['Cumulative Return During Crash'].iloc[-1],UNPdur['Cumulative Return During Crash'].iloc[-1],UALdur['Cumulative Return During Crash'].iloc[-1],UPSdur['Cumulative Return During Crash'].iloc[-1],URIdur['Cumulative Return During Crash'].iloc[-1],VRSKdur['Cumulative Return During Crash'].iloc[-1],WABdur['Cumulative Return During Crash'].iloc[-1],WMdur['Cumulative Return During Crash'].iloc[-1],XYLdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],MMMaft['Cumulative Return After Crash'].iloc[-1],ALKaft['Cumulative Return After Crash'].iloc[-1],ALLEaft['Cumulative Return After Crash'].iloc[-1],AALaft['Cumulative Return After Crash'].iloc[-1],AMEaft['Cumulative Return After Crash'].iloc[-1],AOSaft['Cumulative Return After Crash'].iloc[-1],BAaft['Cumulative Return After Crash'].iloc[-1],CHRWaft['Cumulative Return After Crash'].iloc[-1],CATaft['Cumulative Return After Crash'].iloc[-1],CTASaft['Cumulative Return After Crash'].iloc[-1],CPRTaft['Cumulative Return After Crash'].iloc[-1],CSXaft['Cumulative Return After Crash'].iloc[-1],CMIaft['Cumulative Return After Crash'].iloc[-1],DEaft['Cumulative Return After Crash'].iloc[-1],DALaft['Cumulative Return After Crash'].iloc[-1],DOVaft['Cumulative Return After Crash'].iloc[-1],ETNaft['Cumulative Return After Crash'].iloc[-1],EMRaft['Cumulative Return After Crash'].iloc[-1],EFXaft['Cumulative Return After Crash'].iloc[-1],EXPDaft['Cumulative Return After Crash'].iloc[-1],FASTaft['Cumulative Return After Crash'].iloc[-1],FDXaft['Cumulative Return After Crash'].iloc[-1],FLSaft['Cumulative Return After Crash'].iloc[-1],FTVaft['Cumulative Return After Crash'].iloc[-1],FBHSaft['Cumulative Return After Crash'].iloc[-1],GDaft['Cumulative Return After Crash'].iloc[-1],GEaft['Cumulative Return After Crash'].iloc[-1],GWWaft['Cumulative Return After Crash'].iloc[-1],HONaft['Cumulative Return After Crash'].iloc[-1],HWMaft['Cumulative Return After Crash'].iloc[-1],HIIaft['Cumulative Return After Crash'].iloc[-1],IEXaft['Cumulative Return After Crash'].iloc[-1],INFOaft['Cumulative Return After Crash'].iloc[-1],ITWaft['Cumulative Return After Crash'].iloc[-1],IRaft['Cumulative Return After Crash'].iloc[-1],Jaft['Cumulative Return After Crash'].iloc[-1],JBHTaft['Cumulative Return After Crash'].iloc[-1],JCIaft['Cumulative Return After Crash'].iloc[-1],KSUaft['Cumulative Return After Crash'].iloc[-1],LHXaft['Cumulative Return After Crash'].iloc[-1],LMTaft['Cumulative Return After Crash'].iloc[-1],MASaft['Cumulative Return After Crash'].iloc[-1],NLSNaft['Cumulative Return After Crash'].iloc[-1],NSCaft['Cumulative Return After Crash'].iloc[-1],NOCaft['Cumulative Return After Crash'].iloc[-1],ODFLaft['Cumulative Return After Crash'].iloc[-1],PCARaft['Cumulative Return After Crash'].iloc[-1],PHaft['Cumulative Return After Crash'].iloc[-1],PNRaft['Cumulative Return After Crash'].iloc[-1],PWRaft['Cumulative Return After Crash'].iloc[-1],RTXaft['Cumulative Return After Crash'].iloc[-1],RSGaft['Cumulative Return After Crash'].iloc[-1],RHIaft['Cumulative Return After Crash'].iloc[-1],ROKaft['Cumulative Return After Crash'].iloc[-1],ROLaft['Cumulative Return After Crash'].iloc[-1],ROPaft['Cumulative Return After Crash'].iloc[-1],SNAaft['Cumulative Return After Crash'].iloc[-1],LUVaft['Cumulative Return After Crash'].iloc[-1],SWKaft['Cumulative Return After Crash'].iloc[-1],TXTaft['Cumulative Return After Crash'].iloc[-1],TTaft['Cumulative Return After Crash'].iloc[-1],TDGaft['Cumulative Return After Crash'].iloc[-1],UNPaft['Cumulative Return After Crash'].iloc[-1],UALaft['Cumulative Return After Crash'].iloc[-1],UPSaft['Cumulative Return After Crash'].iloc[-1],URIaft['Cumulative Return After Crash'].iloc[-1],VRSKaft['Cumulative Return After Crash'].iloc[-1],WABaft['Cumulative Return After Crash'].iloc[-1],WMaft['Cumulative Return After Crash'].iloc[-1],XYLaft['Cumulative Return After Crash'].iloc[-1]]}                         

inddf2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','MMM','ALK','ALLE','AAL','AME','AOS','BA','CHRW','CAT','CTAS','CPRT','CSX','CMI','DE','DAL','DOV','ETN','EMR','EFX','EXPD','FAST','FDX','FLS','FTV','FBHS','GD','GE','GWW','HON','HWM','HII','IEX','INFO','ITW','IR','J','JBHT','JCI','KSU','LHX','LMT','MAS','NLSN','NSC','NOC','ODFL','PCAR','PH','PNR','PWR','RTX','RSG','RHI','ROK','ROL','ROP','SNA','LUV','SWK','TXT','TT','TDG','UNP','UAL','UPS','URI','VRSK','WAB','WM','XYL'])

pd.DataFrame(inddf2)
Out[148]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
MMM 1.001615 0.948666 0.743378 1.420294
ALK 0.638374 0.976689 0.367656 1.777778
ALLE 0.947122 1.066180 0.605314 1.467557
AAL 0.678963 1.041271 0.359523 1.813659
... ... ... ... ...
URI 1.071946 1.034478 0.455240 2.276203
VRSK 1.142494 1.164913 0.715391 1.370936
WAB 0.924163 1.030858 0.521690 1.718454
WM 0.994152 1.099149 0.708343 1.276886
XYL 0.965110 1.176174 0.638845 1.284428

71 rows × 4 columns

In [149]:
inddf2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[149]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
URI 1.071946 1.034478 0.455240 2.276203
SWK 0.956681 1.057216 0.444492 2.035818
PH 0.989771 1.090191 0.453177 2.003383
BA 0.588895 0.963995 0.314084 1.944991
ROK 1.166043 1.052818 0.587794 1.884239
... ... ... ... ...
WM 0.994152 1.099149 0.708343 1.276886
FDX 0.924222 1.071749 0.677703 1.272459
NOC 0.996766 1.058390 0.742904 1.267694
UPS 0.915686 0.914227 0.863073 1.160501
LUV 0.684475 1.020796 0.592378 1.131930

71 rows × 4 columns

In [219]:
#Table of Key Statistics of Industrials Stocks
import pandas as pd
import statistics as stats

indmeans = [MMMmean,ALKmean,ALLEmean,AALmean,AMEmean,AOSmean,BAmean,CHRWmean,CATmean,CTASmean,CPRTmean,CSXmean,CMImean,DEmean,DALmean,DOVmean,ETNmean,EMRmean,EFXmean,EXPDmean,FASTmean,FDXmean,FLSmean,FTVmean,FBHSmean,GDmean,GEmean,GWWmean,HONmean,HWMmean,HIImean,IEXmean,INFOmean,ITWmean,IRmean,Jmean,JBHTmean,JCImean,KSUmean,LHXmean,LMTmean,MASmean,NLSNmean,NSCmean,NOCmean,ODFLmean,PCARmean,PHmean,PNRmean,PWRmean,RTXmean,RSGmean,RHImean,ROKmean,ROLmean,ROPmean,SNAmean,LUVmean,SWKmean,TXTmean,TTmean,TDGmean,UNPmean,UALmean,UPSmean,URImean,VRSKmean,WABmean,WMmean,XYLmean]
indaveragemean = stats.mean(indmeans)
indvariances = [MMMvar,ALKvar,ALLEvar,AALvar,AMEvar,AOSvar,BAvar,CHRWvar,CATvar,CTASvar,CPRTvar,CSXvar,CMIvar,DEvar,DALvar,DOVvar,ETNvar,EMRvar,EFXvar,EXPDvar,FASTvar,FDXvar,FLSvar,FTVvar,FBHSvar,GDvar,GEvar,GWWvar,HONvar,HWMvar,HIIvar,IEXvar,INFOvar,ITWvar,IRvar,Jvar,JBHTvar,JCIvar,KSUvar,LHXvar,LMTvar,MASvar,NLSNvar,NSCvar,NOCvar,ODFLvar,PCARvar,PHvar,PNRvar,PWRvar,RTXvar,RSGvar,RHIvar,ROKvar,ROLvar,ROPvar,SNAvar,LUVvar,SWKvar,TXTvar,TTvar,TDGvar,UNPvar,UALvar,UPSvar,URIvar,VRSKvar,WABvar,WMvar,XYLvar]
indaveragevar = stats.mean(indvariances)
indkurtoses = [MMMkurt,ALKkurt,ALLEkurt,AALkurt,AMEkurt,AOSkurt,BAkurt,CHRWkurt,CATkurt,CTASkurt,CPRTkurt,CSXkurt,CMIkurt,DEkurt,DALkurt,DOVkurt,ETNkurt,EMRkurt,EFXkurt,EXPDkurt,FASTkurt,FDXkurt,FLSkurt,FTVkurt,FBHSkurt,GDkurt,GEkurt,GWWkurt,HONkurt,HWMkurt,HIIkurt,IEXkurt,INFOkurt,ITWkurt,IRkurt,Jkurt,JBHTkurt,JCIkurt,KSUkurt,LHXkurt,LMTkurt,MASkurt,NLSNkurt,NSCkurt,NOCkurt,ODFLkurt,PCARkurt,PHkurt,PNRkurt,PWRkurt,RTXkurt,RSGkurt,RHIkurt,ROKkurt,ROLkurt,ROPkurt,SNAkurt,LUVkurt,SWKkurt,TXTkurt,TTkurt,TDGkurt,UNPkurt,UALkurt,UPSkurt,URIkurt,VRSKkurt,WABkurt,WMkurt,XYLkurt]
indaveragekurt = stats.mean(indkurtoses)
indskews = [MMMskew,ALKskew,ALLEskew,AALskew,AMEskew,AOSskew,BAskew,CHRWskew,CATskew,CTASskew,CPRTskew,CSXskew,CMIskew,DEskew,DALskew,DOVskew,ETNskew,EMRskew,EFXskew,EXPDskew,FASTskew,FDXskew,FLSskew,FTVskew,FBHSskew,GDskew,GEskew,GWWskew,HONskew,HWMskew,HIIskew,IEXskew,INFOskew,ITWskew,IRskew,Jskew,JBHTskew,JCIskew,KSUskew,LHXskew,LMTskew,MASskew,NLSNskew,NSCskew,NOCskew,ODFLskew,PCARskew,PHskew,PNRskew,PWRskew,RTXskew,RSGskew,RHIskew,ROKskew,ROLskew,ROPskew,SNAskew,LUVskew,SWKskew,TXTskew,TTskew,TDGskew,UNPskew,UALskew,UPSskew,URIskew,VRSKskew,WABskew,WMskew,XYLskew]
indaverageskew = stats.mean(indskews)

Industrials = {'Mean': [[spmean],[indaveragemean],[MMMmean], [ALKmean],[ALLEmean],[AALmean],[AMEmean],[AOSmean],[BAmean],[CHRWmean],[CATmean],[CTASmean],[CPRTmean],[CSXmean],[CMImean],[DEmean],[DALmean],[DOVmean],[ETNmean],[EMRmean],[EFXmean],[EXPDmean],[FASTmean],[FDXmean],[FLSmean],[FTVmean],[FBHSmean],[GDmean],[GEmean], [GWWmean],[HONmean],[HWMmean],[HIImean],[IEXmean],[INFOmean],[ITWmean],[IRmean],[Jmean],[JBHTmean],[JCImean],[KSUmean],[LHXmean],[LMTmean],[MASmean],[NLSNmean],[NSCmean],[NOCmean],[ODFLmean],[PCARmean],[PHmean],[PNRmean],[PWRmean],[RTXmean],[RSGmean],[RHImean],[ROKmean],[ROLmean],[ROPmean],[SNAmean],[LUVmean],[SWKmean],[TXTmean],[TTmean],[TDGmean],[UNPmean],[UALmean],[UPSmean],[URImean],[VRSKmean],[WABmean],[WMmean],[XYLmean]], 
        'Variance': [[spvar],[indaveragevar],[MMMvar],[ALKvar],[ALLEvar],[AALvar],[AMEvar],[AOSvar],[BAvar],[CHRWvar],[CATvar],[CTASvar],[CPRTvar],[CSXvar],[CMIvar],[DEvar],[DALvar],[DOVvar],[ETNvar],[EMRvar],[EFXvar],[EXPDvar],[FASTvar],[FDXvar],[FLSvar],[FTVvar],[FBHSvar],[GDvar],[GEvar],[GWWvar],[HONvar],[HWMvar],[HIIvar],[IEXvar],[INFOvar],[ITWvar],[IRvar],[Jvar],[JBHTvar],[JCIvar],[KSUvar],[LHXvar],[LMTvar],[MASvar],[NLSNvar],[NSCvar],[NOCvar],[ODFLvar],[PCARvar],[PHvar],[PNRvar],[PWRvar],[RTXvar],[RSGvar],[RHIvar],[ROKvar],[ROLvar],[ROPvar],[SNAvar],[LUVvar],[SWKvar],[TXTvar],[TTvar],[TDGvar],[UNPvar],[UALvar],[UPSvar],[URIvar],[VRSKvar],[WABvar],[WMvar],[XYLvar]], 
          'Kurtosis': [[spkurt],[indaveragekurt],[MMMkurt],[ALKkurt],[ALLEkurt],[AALkurt],[AMEkurt],[AOSkurt],[BAkurt],[CHRWkurt],[CATkurt],[CTASkurt],[CPRTkurt],[CSXkurt],[CMIkurt],[DEkurt],[DALkurt],[DOVkurt],[ETNkurt],[EMRkurt],[EFXkurt],[EXPDkurt],[FASTkurt],[FDXkurt],[FLSkurt],[FTVkurt],[FBHSkurt],[GDkurt],[GEkurt],[GWWkurt],[HONkurt],[HWMkurt],[HIIkurt],[IEXkurt],[INFOkurt],[ITWkurt],[IRkurt],[Jkurt],[JBHTkurt],[JCIkurt],[KSUkurt],[LHXkurt],[LMTkurt],[MASkurt],[NLSNkurt],[NSCkurt],[NOCkurt],[ODFLkurt],[PCARkurt],[PHkurt],[PNRkurt],[PWRkurt],[RTXkurt],[RSGkurt],[RHIkurt],[ROKkurt],[ROLkurt],[ROPkurt],[SNAkurt],[LUVkurt],[SWKkurt],[TXTkurt],[TTkurt],[TDGkurt],[UNPkurt],[UALkurt],[UPSkurt],[URIkurt],[VRSKkurt],[WABkurt],[WMkurt],[XYLkurt]],
          'Skew': [[spskew],[indaverageskew],[MMMskew],[ALKskew],[ALLEskew],[AALskew],[AMEskew],[AOSskew],[BAskew],[CHRWskew],[CATskew],[CTASskew],[CPRTskew],[CSXskew],[CMIskew],[DEskew],[DALskew],[DOVskew],[ETNskew],[EMRskew],[EFXskew],[EXPDskew],[FASTskew],[FDXskew],[FLSskew],[FTVskew],[FBHSskew],[GDskew],[GEskew],[GWWskew],[HONskew],[HWMskew],[HIIskew],[IEXskew],[INFOskew],[ITWskew],[IRskew],[Jskew],[JBHTskew],[JCIskew],[KSUskew],[LHXskew],[LMTskew],[MASskew],[NLSNskew],[NSCskew],[NOCskew],[ODFLskew],[PCARskew],[PHskew],[PNRskew],[PWRskew],[RTXskew],[RSGskew],[RHIskew],[ROKskew],[ROLskew],[ROPskew],[SNAskew],[LUVskew],[SWKskew],[TXTskew],[TTskew],[TDGskew],[UNPskew],[UALskew],[UPSskew],[URIskew],[VRSKskew],[WABskew],[WMskew],[XYLskew]],
        }

inddf = pd.DataFrame(Industrials, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','MMM','ALK','ALLE','AAL','AME','AOS','BA','CHRW','CAT','CTAS','CPRT','CSX','CMI','DE','DAL','DOV','ETN','EMR','EFX','EXPD','FAST','FDX','FLS','FTV','FBHS','GD','GE','GWW','HON','HWM','HII','IEX','INFO','ITW','IR','J','JBHT','JCI','KSU','LHX','LMT','MAS','NLSN','NSC','NOC','ODFL','PCAR','PH','PNR','PWR','RTX','RSG','RHI','ROK','ROL','ROP','SNA','LUV','SWK','TXT','TT','TDG','UNP','UAL','UPS','URI','VRSK','WAB','WM','XYL']
)

pd.DataFrame(inddf)
Out[219]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [0.000426072351837813] [0.001699801551972132] [4.9568321834711035] [-0.024413233149524257]
MMM [0.00045063048379036794] [0.0008842959292585189] [3.293771363695738] [0.1964714637360194]
ALK [-0.001899729446406566] [0.0032424311553503893] [4.573453515068995] [-0.4155100968704701]
ALLE [0.00017448238515145443] [0.0012093837356401528] [3.42937440542676] [-0.2664541964578871]
... ... ... ... ...
URI [0.0019208272840646403] [0.0027645131264272655] [2.9282618056526264] [0.18983956227698243]
VRSK [0.0015321029390109482] [0.0009573113772206527] [5.16935748149947] [0.15353168104641526]
WAB [0.00023494409321915522] [0.0017001663218954419] [3.447635821714001] [-0.5731378674939785]
WM [0.000345202910170309] [0.0007833555519413466] [4.06520312813068] [-0.32540108571272136]
XYL [0.0003705692650848866] [0.001313286519234531] [1.7643986148223343] [0.037301782106169044]

72 rows × 4 columns

In [220]:
inddf.sort_values("Mean", ascending=False)
Out[220]:
Mean Variance Kurtosis Skew
ODFL [0.0032170303384769783] [0.0011044790981743124] [2.581716707243822] [0.2546038562748605]
EFX [0.0025629310418215016] [0.0009935514238917085] [1.3682922039373278] [-0.10794023695768981]
ROL [0.002050446824283995] [0.0007288845996853793] [3.8661278246309703] [-0.2879984887642824]
ROK [0.0020280159513035943] [0.00161931155087779] [6.179977924344009] [-0.04580441105002559]
FAST [0.0020184173662432404] [0.0009331361234314569] [3.650725201051788] [0.33962361764705057]
... ... ... ... ...
BA [-0.0019930506954721657] [0.004417942163762841] [3.8457791217309234] [0.2612199343701605]
FLS [-0.001997087953491067] [0.0020405770135112867] [2.184707041987462] [-0.3822719543568264]
DAL [-0.0021307872461957655] [0.003523439971074017] [5.030559919342223] [-0.24781581348800424]
HWM [-0.002705821542117275] [0.0061025313919219285] [33.8140803080521] [-4.522330169242337]
UAL [-0.002800258539621457] [0.005891187622800697] [3.1702161707730045] [-0.06217615141248271]

72 rows × 4 columns

Consumer Discretionary

In [247]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AAP = web.DataReader('AAP', 'yahoo', start, end)
AAP['returns'] = AAP['Close'].pct_change(1)
AAP['Cumulative Return'] = (1 + AAP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AAPmean = AAP['returns'].mean()
AAPvar = AAP['returns'].var()
AAPkurt = AAP['returns'].kurt()
AAPskew = AAP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AAPbef = web.DataReader('AAP', 'yahoo', start, crashstart)
AAPbef['avgreturnbefore'] = AAPbef['Close'].pct_change(1)
AAPbef['Cumulative Return Before Crash'] = (1 + AAPbef['avgreturnbefore']).cumprod()

#period of crash
AAPdur = web.DataReader('AAP', 'yahoo', crashstart, crashend)
AAPdur['avgreturnduring'] = AAPdur['Close'].pct_change(1)
AAPdur['Cumulative Return During Crash'] = (1 + AAPdur['avgreturnduring']).cumprod()

#period after crash
AAPaft = web.DataReader('AAP', 'yahoo', crashend, end)
AAPaft['avgreturnafter'] = AAPaft['Close'].pct_change(1)
AAPaft['Cumulative Return After Crash'] = (1 + AAPaft['avgreturnafter']).cumprod()
In [248]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AMZN = web.DataReader('AMZN', 'yahoo', start, end)
AMZN['returns'] = AMZN['Close'].pct_change(1)
AMZN['Cumulative Return'] = (1 + AMZN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AMZNmean = AMZN['returns'].mean()
AMZNvar = AMZN['returns'].var()
AMZNkurt = AMZN['returns'].kurt()
AMZNskew = AMZN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AMZNbef = web.DataReader('AMZN', 'yahoo', start, crashstart)
AMZNbef['avgreturnbefore'] = AMZNbef['Close'].pct_change(1)
AMZNbef['Cumulative Return Before Crash'] = (1 + AMZNbef['avgreturnbefore']).cumprod()

#period of crash
AMZNdur = web.DataReader('AMZN', 'yahoo', crashstart, crashend)
AMZNdur['avgreturnduring'] = AMZNdur['Close'].pct_change(1)
AMZNdur['Cumulative Return During Crash'] = (1 + AMZNdur['avgreturnduring']).cumprod()

#period after crash
AMZNaft = web.DataReader('AMZN', 'yahoo', crashend, end)
AMZNaft['avgreturnafter'] = AMZNaft['Close'].pct_change(1)
AMZNaft['Cumulative Return After Crash'] = (1 + AMZNaft['avgreturnafter']).cumprod()
In [249]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
APTV = web.DataReader('APTV', 'yahoo', start, end)
APTV['returns'] = APTV['Close'].pct_change(1)
APTV['Cumulative Return'] = (1 + APTV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
APTVmean = APTV['returns'].mean()
APTVvar = APTV['returns'].var()
APTVkurt = APTV['returns'].kurt()
APTVskew = APTV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
APTVbef = web.DataReader('APTV', 'yahoo', start, crashstart)
APTVbef['avgreturnbefore'] = APTVbef['Close'].pct_change(1)
APTVbef['Cumulative Return Before Crash'] = (1 + APTVbef['avgreturnbefore']).cumprod()

#period of crash
APTVdur = web.DataReader('APTV', 'yahoo', crashstart, crashend)
APTVdur['avgreturnduring'] = APTVdur['Close'].pct_change(1)
APTVdur['Cumulative Return During Crash'] = (1 + APTVdur['avgreturnduring']).cumprod()

#period after crash
APTVaft = web.DataReader('APTV', 'yahoo', crashend, end)
APTVaft['avgreturnafter'] = APTVaft['Close'].pct_change(1)
APTVaft['Cumulative Return After Crash'] = (1 + APTVaft['avgreturnafter']).cumprod()
In [250]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AZO = web.DataReader('AZO', 'yahoo', start, end)
AZO['returns'] = AZO['Close'].pct_change(1)
AZO['Cumulative Return'] = (1 + AZO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AZOmean = AZO['returns'].mean()
AZOvar = AZO['returns'].var()
AZOkurt = AZO['returns'].kurt()
AZOskew = AZO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AZObef = web.DataReader('AZO', 'yahoo', start, crashstart)
AZObef['avgreturnbefore'] = AZObef['Close'].pct_change(1)
AZObef['Cumulative Return Before Crash'] = (1 + AZObef['avgreturnbefore']).cumprod()

#period of crash
AZOdur = web.DataReader('AZO', 'yahoo', crashstart, crashend)
AZOdur['avgreturnduring'] = AZOdur['Close'].pct_change(1)
AZOdur['Cumulative Return During Crash'] = (1 + AZOdur['avgreturnduring']).cumprod()

#period after crash
AZOaft = web.DataReader('AZO', 'yahoo', crashend, end)
AZOaft['avgreturnafter'] = AZOaft['Close'].pct_change(1)
AZOaft['Cumulative Return After Crash'] = (1 + AZOaft['avgreturnafter']).cumprod()
In [251]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BBY = web.DataReader('BBY', 'yahoo', start, end)
BBY['returns'] = BBY['Close'].pct_change(1)
BBY['Cumulative Return'] = (1 + BBY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BBYmean = BBY['returns'].mean()
BBYvar = BBY['returns'].var()
BBYkurt = BBY['returns'].kurt()
BBYskew = BBY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BBYbef = web.DataReader('BBY', 'yahoo', start, crashstart)
BBYbef['avgreturnbefore'] = BBYbef['Close'].pct_change(1)
BBYbef['Cumulative Return Before Crash'] = (1 + BBYbef['avgreturnbefore']).cumprod()

#period of crash
BBYdur = web.DataReader('BBY', 'yahoo', crashstart, crashend)
BBYdur['avgreturnduring'] = BBYdur['Close'].pct_change(1)
BBYdur['Cumulative Return During Crash'] = (1 + BBYdur['avgreturnduring']).cumprod()

#period after crash
BBYaft = web.DataReader('BBY', 'yahoo', crashend, end)
BBYaft['avgreturnafter'] = BBYaft['Close'].pct_change(1)
BBYaft['Cumulative Return After Crash'] = (1 + BBYaft['avgreturnafter']).cumprod()
In [252]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BKNG = web.DataReader('BKNG', 'yahoo', start, end)
BKNG['returns'] = BKNG['Close'].pct_change(1)
BKNG['Cumulative Return'] = (1 + BKNG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BKNGmean = BKNG['returns'].mean()
BKNGvar = BKNG['returns'].var()
BKNGkurt = BKNG['returns'].kurt()
BKNGskew = BKNG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BKNGbef = web.DataReader('BKNG', 'yahoo', start, crashstart)
BKNGbef['avgreturnbefore'] = BKNGbef['Close'].pct_change(1)
BKNGbef['Cumulative Return Before Crash'] = (1 + BKNGbef['avgreturnbefore']).cumprod()

#period of crash
BKNGdur = web.DataReader('BKNG', 'yahoo', crashstart, crashend)
BKNGdur['avgreturnduring'] = BKNGdur['Close'].pct_change(1)
BKNGdur['Cumulative Return During Crash'] = (1 + BKNGdur['avgreturnduring']).cumprod()

#period after crash
BKNGaft = web.DataReader('BKNG', 'yahoo', crashend, end)
BKNGaft['avgreturnafter'] = BKNGaft['Close'].pct_change(1)
BKNGaft['Cumulative Return After Crash'] = (1 + BKNGaft['avgreturnafter']).cumprod()
In [253]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BWA = web.DataReader('BWA', 'yahoo', start, end)
BWA['returns'] = BWA['Close'].pct_change(1)
BWA['Cumulative Return'] = (1 + BWA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BWAmean = BWA['returns'].mean()
BWAvar = BWA['returns'].var()
BWAkurt = BWA['returns'].kurt()
BWAskew = BWA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BWAbef = web.DataReader('BWA', 'yahoo', start, crashstart)
BWAbef['avgreturnbefore'] = BWAbef['Close'].pct_change(1)
BWAbef['Cumulative Return Before Crash'] = (1 + BWAbef['avgreturnbefore']).cumprod()

#period of crash
BWAdur = web.DataReader('BWA', 'yahoo', crashstart, crashend)
BWAdur['avgreturnduring'] = BWAdur['Close'].pct_change(1)
BWAdur['Cumulative Return During Crash'] = (1 + BWAdur['avgreturnduring']).cumprod()

#period after crash
BWAaft = web.DataReader('BWA', 'yahoo', crashend, end)
BWAaft['avgreturnafter'] = BWAaft['Close'].pct_change(1)
BWAaft['Cumulative Return After Crash'] = (1 + BWAaft['avgreturnafter']).cumprod()
In [254]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KMX = web.DataReader('KMX', 'yahoo', start, end)
KMX['returns'] = KMX['Close'].pct_change(1)
KMX['Cumulative Return'] = (1 + KMX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KMXmean = KMX['returns'].mean()
KMXvar = KMX['returns'].var()
KMXkurt = KMX['returns'].kurt()
KMXskew = KMX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KMXbef = web.DataReader('KMX', 'yahoo', start, crashstart)
KMXbef['avgreturnbefore'] = KMXbef['Close'].pct_change(1)
KMXbef['Cumulative Return Before Crash'] = (1 + KMXbef['avgreturnbefore']).cumprod()

#period of crash
KMXdur = web.DataReader('KMX', 'yahoo', crashstart, crashend)
KMXdur['avgreturnduring'] = KMXdur['Close'].pct_change(1)
KMXdur['Cumulative Return During Crash'] = (1 + KMXdur['avgreturnduring']).cumprod()

#period after crash
KMXaft = web.DataReader('KMX', 'yahoo', crashend, end)
KMXaft['avgreturnafter'] = KMXaft['Close'].pct_change(1)
KMXaft['Cumulative Return After Crash'] = (1 + KMXaft['avgreturnafter']).cumprod()
In [255]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CCL = web.DataReader('CCL', 'yahoo', start, end)
CCL['returns'] = CCL['Close'].pct_change(1)
CCL['Cumulative Return'] = (1 + CCL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CCLmean = CCL['returns'].mean()
CCLvar = CCL['returns'].var()
CCLkurt = CCL['returns'].kurt()
CCLskew = CCL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CCLbef = web.DataReader('CCL', 'yahoo', start, crashstart)
CCLbef['avgreturnbefore'] = CCLbef['Close'].pct_change(1)
CCLbef['Cumulative Return Before Crash'] = (1 + CCLbef['avgreturnbefore']).cumprod()

#period of crash
CCLdur = web.DataReader('CCL', 'yahoo', crashstart, crashend)
CCLdur['avgreturnduring'] = CCLdur['Close'].pct_change(1)
CCLdur['Cumulative Return During Crash'] = (1 + CCLdur['avgreturnduring']).cumprod()

#period after crash
CCLaft = web.DataReader('CCL', 'yahoo', crashend, end)
CCLaft['avgreturnafter'] = CCLaft['Close'].pct_change(1)
CCLaft['Cumulative Return After Crash'] = (1 + CCLaft['avgreturnafter']).cumprod()
In [256]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CMG = web.DataReader('CMG', 'yahoo', start, end)
CMG['returns'] = CMG['Close'].pct_change(1)
CMG['Cumulative Return'] = (1 + CMG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CMGmean = CMG['returns'].mean()
CMGvar = CMG['returns'].var()
CMGkurt = CMG['returns'].kurt()
CMGskew = CMG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CMGbef = web.DataReader('CMG', 'yahoo', start, crashstart)
CMGbef['avgreturnbefore'] = CMGbef['Close'].pct_change(1)
CMGbef['Cumulative Return Before Crash'] = (1 + CMGbef['avgreturnbefore']).cumprod()

#period of crash
CMGdur = web.DataReader('CMG', 'yahoo', crashstart, crashend)
CMGdur['avgreturnduring'] = CMGdur['Close'].pct_change(1)
CMGdur['Cumulative Return During Crash'] = (1 + CMGdur['avgreturnduring']).cumprod()

#period after crash
CMGaft = web.DataReader('CMG', 'yahoo', crashend, end)
CMGaft['avgreturnafter'] = CMGaft['Close'].pct_change(1)
CMGaft['Cumulative Return After Crash'] = (1 + CMGaft['avgreturnafter']).cumprod()
In [257]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DHI = web.DataReader('DHI', 'yahoo', start, end)
DHI['returns'] = DHI['Close'].pct_change(1)
DHI['Cumulative Return'] = (1 + DHI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DHImean = DHI['returns'].mean()
DHIvar = DHI['returns'].var()
DHIkurt = DHI['returns'].kurt()
DHIskew = DHI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DHIbef = web.DataReader('DHI', 'yahoo', start, crashstart)
DHIbef['avgreturnbefore'] = DHIbef['Close'].pct_change(1)
DHIbef['Cumulative Return Before Crash'] = (1 + DHIbef['avgreturnbefore']).cumprod()

#period of crash
DHIdur = web.DataReader('DHI', 'yahoo', crashstart, crashend)
DHIdur['avgreturnduring'] = DHIdur['Close'].pct_change(1)
DHIdur['Cumulative Return During Crash'] = (1 + DHIdur['avgreturnduring']).cumprod()

#period after crash
DHIaft = web.DataReader('DHI', 'yahoo', crashend, end)
DHIaft['avgreturnafter'] = DHIaft['Close'].pct_change(1)
DHIaft['Cumulative Return After Crash'] = (1 + DHIaft['avgreturnafter']).cumprod()
In [258]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DRI = web.DataReader('DRI', 'yahoo', start, end)
DRI['returns'] = DRI['Close'].pct_change(1)
DRI['Cumulative Return'] = (1 + DRI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DRImean = DRI['returns'].mean()
DRIvar = DRI['returns'].var()
DRIkurt = DRI['returns'].kurt()
DRIskew = DRI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DRIbef = web.DataReader('DRI', 'yahoo', start, crashstart)
DRIbef['avgreturnbefore'] = DRIbef['Close'].pct_change(1)
DRIbef['Cumulative Return Before Crash'] = (1 + DRIbef['avgreturnbefore']).cumprod()

#period of crash
DRIdur = web.DataReader('DRI', 'yahoo', crashstart, crashend)
DRIdur['avgreturnduring'] = DRIdur['Close'].pct_change(1)
DRIdur['Cumulative Return During Crash'] = (1 + DRIdur['avgreturnduring']).cumprod()

#period after crash
DRIaft = web.DataReader('DRI', 'yahoo', crashend, end)
DRIaft['avgreturnafter'] = DRIaft['Close'].pct_change(1)
DRIaft['Cumulative Return After Crash'] = (1 + DRIaft['avgreturnafter']).cumprod()
In [259]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DG = web.DataReader('DG', 'yahoo', start, end)
DG['returns'] = DG['Close'].pct_change(1)
DG['Cumulative Return'] = (1 + DG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DGmean = DG['returns'].mean()
DGvar = DG['returns'].var()
DGkurt = DG['returns'].kurt()
DGskew = DG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DGbef = web.DataReader('DG', 'yahoo', start, crashstart)
DGbef['avgreturnbefore'] = DGbef['Close'].pct_change(1)
DGbef['Cumulative Return Before Crash'] = (1 + DGbef['avgreturnbefore']).cumprod()

#period of crash
DGdur = web.DataReader('DG', 'yahoo', crashstart, crashend)
DGdur['avgreturnduring'] = DGdur['Close'].pct_change(1)
DGdur['Cumulative Return During Crash'] = (1 + DGdur['avgreturnduring']).cumprod()

#period after crash
DGaft = web.DataReader('DG', 'yahoo', crashend, end)
DGaft['avgreturnafter'] = DGaft['Close'].pct_change(1)
DGaft['Cumulative Return After Crash'] = (1 + DGaft['avgreturnafter']).cumprod()
In [260]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DLTR = web.DataReader('DLTR', 'yahoo', start, end)
DLTR['returns'] = DLTR['Close'].pct_change(1)
DLTR['Cumulative Return'] = (1 + DLTR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DLTRmean = DLTR['returns'].mean()
DLTRvar = DLTR['returns'].var()
DLTRkurt = DLTR['returns'].kurt()
DLTRskew = DLTR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DLTRbef = web.DataReader('DLTR', 'yahoo', start, crashstart)
DLTRbef['avgreturnbefore'] = DLTRbef['Close'].pct_change(1)
DLTRbef['Cumulative Return Before Crash'] = (1 + DLTRbef['avgreturnbefore']).cumprod()

#period of crash
DLTRdur = web.DataReader('DLTR', 'yahoo', crashstart, crashend)
DLTRdur['avgreturnduring'] = DLTRdur['Close'].pct_change(1)
DLTRdur['Cumulative Return During Crash'] = (1 + DLTRdur['avgreturnduring']).cumprod()

#period after crash
DLTRaft = web.DataReader('DLTR', 'yahoo', crashend, end)
DLTRaft['avgreturnafter'] = DLTRaft['Close'].pct_change(1)
DLTRaft['Cumulative Return After Crash'] = (1 + DLTRaft['avgreturnafter']).cumprod()
In [261]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DPZ = web.DataReader('DPZ', 'yahoo', start, end)
DPZ['returns'] = DPZ['Close'].pct_change(1)
DPZ['Cumulative Return'] = (1 + DPZ['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DPZmean = DPZ['returns'].mean()
DPZvar = DPZ['returns'].var()
DPZkurt = DPZ['returns'].kurt()
DPZskew = DPZ['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DPZbef = web.DataReader('DPZ', 'yahoo', start, crashstart)
DPZbef['avgreturnbefore'] = DPZbef['Close'].pct_change(1)
DPZbef['Cumulative Return Before Crash'] = (1 + DPZbef['avgreturnbefore']).cumprod()

#period of crash
DPZdur = web.DataReader('DPZ', 'yahoo', crashstart, crashend)
DPZdur['avgreturnduring'] = DPZdur['Close'].pct_change(1)
DPZdur['Cumulative Return During Crash'] = (1 + DPZdur['avgreturnduring']).cumprod()

#period after crash
DPZaft = web.DataReader('DPZ', 'yahoo', crashend, end)
DPZaft['avgreturnafter'] = DPZaft['Close'].pct_change(1)
DPZaft['Cumulative Return After Crash'] = (1 + DPZaft['avgreturnafter']).cumprod()
In [262]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EBAY = web.DataReader('EBAY', 'yahoo', start, end)
EBAY['returns'] = EBAY['Close'].pct_change(1)
EBAY['Cumulative Return'] = (1 + EBAY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EBAYmean = EBAY['returns'].mean()
EBAYvar = EBAY['returns'].var()
EBAYkurt = EBAY['returns'].kurt()
EBAYskew = EBAY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EBAYbef = web.DataReader('EBAY', 'yahoo', start, crashstart)
EBAYbef['avgreturnbefore'] = EBAYbef['Close'].pct_change(1)
EBAYbef['Cumulative Return Before Crash'] = (1 + EBAYbef['avgreturnbefore']).cumprod()

#period of crash
EBAYdur = web.DataReader('EBAY', 'yahoo', crashstart, crashend)
EBAYdur['avgreturnduring'] = EBAYdur['Close'].pct_change(1)
EBAYdur['Cumulative Return During Crash'] = (1 + EBAYdur['avgreturnduring']).cumprod()

#period after crash
EBAYaft = web.DataReader('EBAY', 'yahoo', crashend, end)
EBAYaft['avgreturnafter'] = EBAYaft['Close'].pct_change(1)
EBAYaft['Cumulative Return After Crash'] = (1 + EBAYaft['avgreturnafter']).cumprod()
In [263]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EXPE = web.DataReader('EXPE', 'yahoo', start, end)
EXPE['returns'] = EXPE['Close'].pct_change(1)
EXPE['Cumulative Return'] = (1 + EXPE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EXPEmean = EXPE['returns'].mean()
EXPEvar = EXPE['returns'].var()
EXPEkurt = EXPE['returns'].kurt()
EXPEskew = EXPE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EXPEbef = web.DataReader('EXPE', 'yahoo', start, crashstart)
EXPEbef['avgreturnbefore'] = EXPEbef['Close'].pct_change(1)
EXPEbef['Cumulative Return Before Crash'] = (1 + EXPEbef['avgreturnbefore']).cumprod()

#period of crash
EXPEdur = web.DataReader('EXPE', 'yahoo', crashstart, crashend)
EXPEdur['avgreturnduring'] = EXPEdur['Close'].pct_change(1)
EXPEdur['Cumulative Return During Crash'] = (1 + EXPEdur['avgreturnduring']).cumprod()

#period after crash
EXPEaft = web.DataReader('EXPE', 'yahoo', crashend, end)
EXPEaft['avgreturnafter'] = EXPEaft['Close'].pct_change(1)
EXPEaft['Cumulative Return After Crash'] = (1 + EXPEaft['avgreturnafter']).cumprod()
In [264]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
F = web.DataReader('F', 'yahoo', start, end)
F['returns'] = F['Close'].pct_change(1)
F['Cumulative Return'] = (1 + F['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
Fmean = F['returns'].mean()
Fvar = F['returns'].var()
Fkurt = F['returns'].kurt()
Fskew = F['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
Fbef = web.DataReader('F', 'yahoo', start, crashstart)
Fbef['avgreturnbefore'] = Fbef['Close'].pct_change(1)
Fbef['Cumulative Return Before Crash'] = (1 + Fbef['avgreturnbefore']).cumprod()

#period of crash
Fdur = web.DataReader('F', 'yahoo', crashstart, crashend)
Fdur['avgreturnduring'] = Fdur['Close'].pct_change(1)
Fdur['Cumulative Return During Crash'] = (1 + Fdur['avgreturnduring']).cumprod()

#period after crash
Faft = web.DataReader('F', 'yahoo', crashend, end)
Faft['avgreturnafter'] = Faft['Close'].pct_change(1)
Faft['Cumulative Return After Crash'] = (1 + Faft['avgreturnafter']).cumprod()
In [265]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GPS = web.DataReader('GPS', 'yahoo', start, end)
GPS['returns'] = GPS['Close'].pct_change(1)
GPS['Cumulative Return'] = (1 + GPS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GPSmean = GPS['returns'].mean()
GPSvar = GPS['returns'].var()
GPSkurt = GPS['returns'].kurt()
GPSskew = GPS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GPSbef = web.DataReader('GPS', 'yahoo', start, crashstart)
GPSbef['avgreturnbefore'] = GPSbef['Close'].pct_change(1)
GPSbef['Cumulative Return Before Crash'] = (1 + GPSbef['avgreturnbefore']).cumprod()

#period of crash
GPSdur = web.DataReader('GPS', 'yahoo', crashstart, crashend)
GPSdur['avgreturnduring'] = GPSdur['Close'].pct_change(1)
GPSdur['Cumulative Return During Crash'] = (1 + GPSdur['avgreturnduring']).cumprod()

#period after crash
GPSaft = web.DataReader('GPS', 'yahoo', crashend, end)
GPSaft['avgreturnafter'] = GPSaft['Close'].pct_change(1)
GPSaft['Cumulative Return After Crash'] = (1 + GPSaft['avgreturnafter']).cumprod()
In [266]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GRMN = web.DataReader('GRMN', 'yahoo', start, end)
GRMN['returns'] = GRMN['Close'].pct_change(1)
GRMN['Cumulative Return'] = (1 + GRMN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GRMNmean = GRMN['returns'].mean()
GRMNvar = GRMN['returns'].var()
GRMNkurt = GRMN['returns'].kurt()
GRMNskew = GRMN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GRMNbef = web.DataReader('GRMN', 'yahoo', start, crashstart)
GRMNbef['avgreturnbefore'] = GRMNbef['Close'].pct_change(1)
GRMNbef['Cumulative Return Before Crash'] = (1 + GRMNbef['avgreturnbefore']).cumprod()

#period of crash
GRMNdur = web.DataReader('GRMN', 'yahoo', crashstart, crashend)
GRMNdur['avgreturnduring'] = GRMNdur['Close'].pct_change(1)
GRMNdur['Cumulative Return During Crash'] = (1 + GRMNdur['avgreturnduring']).cumprod()

#period after crash
GRMNaft = web.DataReader('GRMN', 'yahoo', crashend, end)
GRMNaft['avgreturnafter'] = GRMNaft['Close'].pct_change(1)
GRMNaft['Cumulative Return After Crash'] = (1 + GRMNaft['avgreturnafter']).cumprod()
In [267]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GM = web.DataReader('GM', 'yahoo', start, end)
GM['returns'] = GM['Close'].pct_change(1)
GM['Cumulative Return'] = (1 + GM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GMmean = GM['returns'].mean()
GMvar = GM['returns'].var()
GMkurt = GM['returns'].kurt()
GMskew = GM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GMbef = web.DataReader('GM', 'yahoo', start, crashstart)
GMbef['avgreturnbefore'] = GMbef['Close'].pct_change(1)
GMbef['Cumulative Return Before Crash'] = (1 + GMbef['avgreturnbefore']).cumprod()

#period of crash
GMdur = web.DataReader('GM', 'yahoo', crashstart, crashend)
GMdur['avgreturnduring'] = GMdur['Close'].pct_change(1)
GMdur['Cumulative Return During Crash'] = (1 + GMdur['avgreturnduring']).cumprod()

#period after crash
GMaft = web.DataReader('GM', 'yahoo', crashend, end)
GMaft['avgreturnafter'] = GMaft['Close'].pct_change(1)
GMaft['Cumulative Return After Crash'] = (1 + GMaft['avgreturnafter']).cumprod()
In [268]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GPC = web.DataReader('GPC', 'yahoo', start, end)
GPC['returns'] = GPC['Close'].pct_change(1)
GPC['Cumulative Return'] = (1 + GPC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GPCmean = GPC['returns'].mean()
GPCvar = GPC['returns'].var()
GPCkurt = GPC['returns'].kurt()
GPCskew = GPC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GPCbef = web.DataReader('GPC', 'yahoo', start, crashstart)
GPCbef['avgreturnbefore'] = GPCbef['Close'].pct_change(1)
GPCbef['Cumulative Return Before Crash'] = (1 + GPCbef['avgreturnbefore']).cumprod()

#period of crash
GPCdur = web.DataReader('GPC', 'yahoo', crashstart, crashend)
GPCdur['avgreturnduring'] = GPCdur['Close'].pct_change(1)
GPCdur['Cumulative Return During Crash'] = (1 + GPCdur['avgreturnduring']).cumprod()

#period after crash
GPCaft = web.DataReader('GPC', 'yahoo', crashend, end)
GPCaft['avgreturnafter'] = GPCaft['Close'].pct_change(1)
GPCaft['Cumulative Return After Crash'] = (1 + GPCaft['avgreturnafter']).cumprod()
In [269]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HRB = web.DataReader('HRB', 'yahoo', start, end)
HRB['returns'] = HRB['Close'].pct_change(1)
HRB['Cumulative Return'] = (1 + HRB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HRBmean = HRB['returns'].mean()
HRBvar = HRB['returns'].var()
HRBkurt = HRB['returns'].kurt()
HRBskew = HRB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HRBbef = web.DataReader('HRB', 'yahoo', start, crashstart)
HRBbef['avgreturnbefore'] = HRBbef['Close'].pct_change(1)
HRBbef['Cumulative Return Before Crash'] = (1 + HRBbef['avgreturnbefore']).cumprod()

#period of crash
HRBdur = web.DataReader('HRB', 'yahoo', crashstart, crashend)
HRBdur['avgreturnduring'] = HRBdur['Close'].pct_change(1)
HRBdur['Cumulative Return During Crash'] = (1 + HRBdur['avgreturnduring']).cumprod()

#period after crash
HRBaft = web.DataReader('HRB', 'yahoo', crashend, end)
HRBaft['avgreturnafter'] = HRBaft['Close'].pct_change(1)
HRBaft['Cumulative Return After Crash'] = (1 + HRBaft['avgreturnafter']).cumprod()
In [270]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HBI = web.DataReader('HBI', 'yahoo', start, end)
HBI['returns'] = HBI['Close'].pct_change(1)
HBI['Cumulative Return'] = (1 + HBI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HBImean = HBI['returns'].mean()
HBIvar = HBI['returns'].var()
HBIkurt = HBI['returns'].kurt()
HBIskew = HBI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HBIbef = web.DataReader('HBI', 'yahoo', start, crashstart)
HBIbef['avgreturnbefore'] = HBIbef['Close'].pct_change(1)
HBIbef['Cumulative Return Before Crash'] = (1 + HBIbef['avgreturnbefore']).cumprod()

#period of crash
HBIdur = web.DataReader('HBI', 'yahoo', crashstart, crashend)
HBIdur['avgreturnduring'] = HBIdur['Close'].pct_change(1)
HBIdur['Cumulative Return During Crash'] = (1 + HBIdur['avgreturnduring']).cumprod()

#period after crash
HBIaft = web.DataReader('HBI', 'yahoo', crashend, end)
HBIaft['avgreturnafter'] = HBIaft['Close'].pct_change(1)
HBIaft['Cumulative Return After Crash'] = (1 + HBIaft['avgreturnafter']).cumprod()
In [271]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HOG = web.DataReader('HOG', 'yahoo', start, end)
HOG['returns'] = HOG['Close'].pct_change(1)
HOG['Cumulative Return'] = (1 + HOG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HOGmean = HOG['returns'].mean()
HOGvar = HOG['returns'].var()
HOGkurt = HOG['returns'].kurt()
HOGskew = HOG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HOGbef = web.DataReader('HOG', 'yahoo', start, crashstart)
HOGbef['avgreturnbefore'] = HOGbef['Close'].pct_change(1)
HOGbef['Cumulative Return Before Crash'] = (1 + HOGbef['avgreturnbefore']).cumprod()

#period of crash
HOGdur = web.DataReader('HOG', 'yahoo', crashstart, crashend)
HOGdur['avgreturnduring'] = HOGdur['Close'].pct_change(1)
HOGdur['Cumulative Return During Crash'] = (1 + HOGdur['avgreturnduring']).cumprod()

#period after crash
HOGaft = web.DataReader('HOG', 'yahoo', crashend, end)
HOGaft['avgreturnafter'] = HOGaft['Close'].pct_change(1)
HOGaft['Cumulative Return After Crash'] = (1 + HOGaft['avgreturnafter']).cumprod()
In [272]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HAS = web.DataReader('HAS', 'yahoo', start, end)
HAS['returns'] = HAS['Close'].pct_change(1)
HAS['Cumulative Return'] = (1 + HAS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HASmean = HAS['returns'].mean()
HASvar = HAS['returns'].var()
HASkurt = HAS['returns'].kurt()
HASskew = HAS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HASbef = web.DataReader('HAS', 'yahoo', start, crashstart)
HASbef['avgreturnbefore'] = HASbef['Close'].pct_change(1)
HASbef['Cumulative Return Before Crash'] = (1 + HASbef['avgreturnbefore']).cumprod()

#period of crash
HASdur = web.DataReader('HAS', 'yahoo', crashstart, crashend)
HASdur['avgreturnduring'] = HASdur['Close'].pct_change(1)
HASdur['Cumulative Return During Crash'] = (1 + HASdur['avgreturnduring']).cumprod()

#period after crash
HASaft = web.DataReader('HAS', 'yahoo', crashend, end)
HASaft['avgreturnafter'] = HASaft['Close'].pct_change(1)
HASaft['Cumulative Return After Crash'] = (1 + HASaft['avgreturnafter']).cumprod()
In [273]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HLT = web.DataReader('HLT', 'yahoo', start, end)
HLT['returns'] = HLT['Close'].pct_change(1)
HLT['Cumulative Return'] = (1 + HLT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HLTmean = HLT['returns'].mean()
HLTvar = HLT['returns'].var()
HLTkurt = HLT['returns'].kurt()
HLTskew = HLT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HLTbef = web.DataReader('HLT', 'yahoo', start, crashstart)
HLTbef['avgreturnbefore'] = HLTbef['Close'].pct_change(1)
HLTbef['Cumulative Return Before Crash'] = (1 + HLTbef['avgreturnbefore']).cumprod()

#period of crash
HLTdur = web.DataReader('HLT', 'yahoo', crashstart, crashend)
HLTdur['avgreturnduring'] = HLTdur['Close'].pct_change(1)
HLTdur['Cumulative Return During Crash'] = (1 + HLTdur['avgreturnduring']).cumprod()

#period after crash
HLTaft = web.DataReader('HLT', 'yahoo', crashend, end)
HLTaft['avgreturnafter'] = HLTaft['Close'].pct_change(1)
HLTaft['Cumulative Return After Crash'] = (1 + HLTaft['avgreturnafter']).cumprod()
In [274]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HD = web.DataReader('HD', 'yahoo', start, end)
HD['returns'] = HD['Close'].pct_change(1)
HD['Cumulative Return'] = (1 + HD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HDmean = HD['returns'].mean()
HDvar = HD['returns'].var()
HDkurt = HD['returns'].kurt()
HDskew = HD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HDbef = web.DataReader('HD', 'yahoo', start, crashstart)
HDbef['avgreturnbefore'] = HDbef['Close'].pct_change(1)
HDbef['Cumulative Return Before Crash'] = (1 + HDbef['avgreturnbefore']).cumprod()

#period of crash
HDdur = web.DataReader('HD', 'yahoo', crashstart, crashend)
HDdur['avgreturnduring'] = HDdur['Close'].pct_change(1)
HDdur['Cumulative Return During Crash'] = (1 + HDdur['avgreturnduring']).cumprod()

#period after crash
HDaft = web.DataReader('HD', 'yahoo', crashend, end)
HDaft['avgreturnafter'] = HDaft['Close'].pct_change(1)
HDaft['Cumulative Return After Crash'] = (1 + HDaft['avgreturnafter']).cumprod()
In [275]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KSS = web.DataReader('KSS', 'yahoo', start, end)
KSS['returns'] = KSS['Close'].pct_change(1)
KSS['Cumulative Return'] = (1 + KSS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KSSmean = KSS['returns'].mean()
KSSvar = KSS['returns'].var()
KSSkurt = KSS['returns'].kurt()
KSSskew = KSS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KSSbef = web.DataReader('KSS', 'yahoo', start, crashstart)
KSSbef['avgreturnbefore'] = KSSbef['Close'].pct_change(1)
KSSbef['Cumulative Return Before Crash'] = (1 + KSSbef['avgreturnbefore']).cumprod()

#period of crash
KSSdur = web.DataReader('KSS', 'yahoo', crashstart, crashend)
KSSdur['avgreturnduring'] = KSSdur['Close'].pct_change(1)
KSSdur['Cumulative Return During Crash'] = (1 + KSSdur['avgreturnduring']).cumprod()

#period after crash
KSSaft = web.DataReader('KSS', 'yahoo', crashend, end)
KSSaft['avgreturnafter'] = KSSaft['Close'].pct_change(1)
KSSaft['Cumulative Return After Crash'] = (1 + KSSaft['avgreturnafter']).cumprod()
In [276]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LB = web.DataReader('LB', 'yahoo', start, end)
LB['returns'] = LB['Close'].pct_change(1)
LB['Cumulative Return'] = (1 + LB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LBmean = LB['returns'].mean()
LBvar = LB['returns'].var()
LBkurt = LB['returns'].kurt()
LBskew = LB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LBbef = web.DataReader('LB', 'yahoo', start, crashstart)
LBbef['avgreturnbefore'] = LBbef['Close'].pct_change(1)
LBbef['Cumulative Return Before Crash'] = (1 + LBbef['avgreturnbefore']).cumprod()

#period of crash
LBdur = web.DataReader('LB', 'yahoo', crashstart, crashend)
LBdur['avgreturnduring'] = LBdur['Close'].pct_change(1)
LBdur['Cumulative Return During Crash'] = (1 + LBdur['avgreturnduring']).cumprod()

#period after crash
LBaft = web.DataReader('LB', 'yahoo', crashend, end)
LBaft['avgreturnafter'] = LBaft['Close'].pct_change(1)
LBaft['Cumulative Return After Crash'] = (1 + LBaft['avgreturnafter']).cumprod()
In [277]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LVS = web.DataReader('LVS', 'yahoo', start, end)
LVS['returns'] = LVS['Close'].pct_change(1)
LVS['Cumulative Return'] = (1 + LVS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LVSmean = LVS['returns'].mean()
LVSvar = LVS['returns'].var()
LVSkurt = LVS['returns'].kurt()
LVSskew = LVS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LVSbef = web.DataReader('LVS', 'yahoo', start, crashstart)
LVSbef['avgreturnbefore'] = LVSbef['Close'].pct_change(1)
LVSbef['Cumulative Return Before Crash'] = (1 + LVSbef['avgreturnbefore']).cumprod()

#period of crash
LVSdur = web.DataReader('LVS', 'yahoo', crashstart, crashend)
LVSdur['avgreturnduring'] = LVSdur['Close'].pct_change(1)
LVSdur['Cumulative Return During Crash'] = (1 + LVSdur['avgreturnduring']).cumprod()

#period after crash
LVSaft = web.DataReader('LVS', 'yahoo', crashend, end)
LVSaft['avgreturnafter'] = LVSaft['Close'].pct_change(1)
LVSaft['Cumulative Return After Crash'] = (1 + LVSaft['avgreturnafter']).cumprod()
In [278]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LEG = web.DataReader('LEG', 'yahoo', start, end)
LEG['returns'] = LEG['Close'].pct_change(1)
LEG['Cumulative Return'] = (1 + LEG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LEGmean = LEG['returns'].mean()
LEGvar = LEG['returns'].var()
LEGkurt = LEG['returns'].kurt()
LEGskew = LEG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LEGbef = web.DataReader('LEG', 'yahoo', start, crashstart)
LEGbef['avgreturnbefore'] = LEGbef['Close'].pct_change(1)
LEGbef['Cumulative Return Before Crash'] = (1 + LEGbef['avgreturnbefore']).cumprod()

#period of crash
LEGdur = web.DataReader('LEG', 'yahoo', crashstart, crashend)
LEGdur['avgreturnduring'] = LEGdur['Close'].pct_change(1)
LEGdur['Cumulative Return During Crash'] = (1 + LEGdur['avgreturnduring']).cumprod()

#period after crash
LEGaft = web.DataReader('LEG', 'yahoo', crashend, end)
LEGaft['avgreturnafter'] = LEGaft['Close'].pct_change(1)
LEGaft['Cumulative Return After Crash'] = (1 + LEGaft['avgreturnafter']).cumprod()
In [279]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LEN = web.DataReader('LEN', 'yahoo', start, end)
LEN['returns'] = LEN['Close'].pct_change(1)
LEN['Cumulative Return'] = (1 + LEN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LENmean = LEN['returns'].mean()
LENvar = LEN['returns'].var()
LENkurt = LEN['returns'].kurt()
LENskew = LEN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LENbef = web.DataReader('LEN', 'yahoo', start, crashstart)
LENbef['avgreturnbefore'] = LENbef['Close'].pct_change(1)
LENbef['Cumulative Return Before Crash'] = (1 + LENbef['avgreturnbefore']).cumprod()

#period of crash
LENdur = web.DataReader('LEN', 'yahoo', crashstart, crashend)
LENdur['avgreturnduring'] = LENdur['Close'].pct_change(1)
LENdur['Cumulative Return During Crash'] = (1 + LENdur['avgreturnduring']).cumprod()

#period after crash
LENaft = web.DataReader('LEN', 'yahoo', crashend, end)
LENaft['avgreturnafter'] = LENaft['Close'].pct_change(1)
LENaft['Cumulative Return After Crash'] = (1 + LENaft['avgreturnafter']).cumprod()
In [280]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LKQ = web.DataReader('LKQ', 'yahoo', start, end)
LKQ['returns'] = LKQ['Close'].pct_change(1)
LKQ['Cumulative Return'] = (1 + LKQ['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LKQmean = LKQ['returns'].mean()
LKQvar = LKQ['returns'].var()
LKQkurt = LKQ['returns'].kurt()
LKQskew = LKQ['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LKQbef = web.DataReader('LKQ', 'yahoo', start, crashstart)
LKQbef['avgreturnbefore'] = LKQbef['Close'].pct_change(1)
LKQbef['Cumulative Return Before Crash'] = (1 + LKQbef['avgreturnbefore']).cumprod()

#period of crash
LKQdur = web.DataReader('LKQ', 'yahoo', crashstart, crashend)
LKQdur['avgreturnduring'] = LKQdur['Close'].pct_change(1)
LKQdur['Cumulative Return During Crash'] = (1 + LKQdur['avgreturnduring']).cumprod()

#period after crash
LKQaft = web.DataReader('LKQ', 'yahoo', crashend, end)
LKQaft['avgreturnafter'] = LKQaft['Close'].pct_change(1)
LKQaft['Cumulative Return After Crash'] = (1 + LKQaft['avgreturnafter']).cumprod()
In [281]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LOW = web.DataReader('LOW', 'yahoo', start, end)
LOW['returns'] = LOW['Close'].pct_change(1)
LOW['Cumulative Return'] = (1 + LOW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LOWmean = LOW['returns'].mean()
LOWvar = LOW['returns'].var()
LOWkurt = LOW['returns'].kurt()
LOWskew = LOW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LOWbef = web.DataReader('LOW', 'yahoo', start, crashstart)
LOWbef['avgreturnbefore'] = LOWbef['Close'].pct_change(1)
LOWbef['Cumulative Return Before Crash'] = (1 + LOWbef['avgreturnbefore']).cumprod()

#period of crash
LOWdur = web.DataReader('LOW', 'yahoo', crashstart, crashend)
LOWdur['avgreturnduring'] = LOWdur['Close'].pct_change(1)
LOWdur['Cumulative Return During Crash'] = (1 + LOWdur['avgreturnduring']).cumprod()

#period after crash
LOWaft = web.DataReader('LOW', 'yahoo', crashend, end)
LOWaft['avgreturnafter'] = LOWaft['Close'].pct_change(1)
LOWaft['Cumulative Return After Crash'] = (1 + LOWaft['avgreturnafter']).cumprod()
In [282]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MAR = web.DataReader('MAR', 'yahoo', start, end)
MAR['returns'] = MAR['Close'].pct_change(1)
MAR['Cumulative Return'] = (1 + MAR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MARmean = MAR['returns'].mean()
MARvar = MAR['returns'].var()
MARkurt = MAR['returns'].kurt()
MARskew = MAR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MARbef = web.DataReader('MAR', 'yahoo', start, crashstart)
MARbef['avgreturnbefore'] = MARbef['Close'].pct_change(1)
MARbef['Cumulative Return Before Crash'] = (1 + MARbef['avgreturnbefore']).cumprod()

#period of crash
MARdur = web.DataReader('MAR', 'yahoo', crashstart, crashend)
MARdur['avgreturnduring'] = MARdur['Close'].pct_change(1)
MARdur['Cumulative Return During Crash'] = (1 + MARdur['avgreturnduring']).cumprod()

#period after crash
MARaft = web.DataReader('MAR', 'yahoo', crashend, end)
MARaft['avgreturnafter'] = MARaft['Close'].pct_change(1)
MARaft['Cumulative Return After Crash'] = (1 + MARaft['avgreturnafter']).cumprod()
In [283]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MCD = web.DataReader('MCD', 'yahoo', start, end)
MCD['returns'] = MCD['Close'].pct_change(1)
MCD['Cumulative Return'] = (1 + MCD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MCDmean = MCD['returns'].mean()
MCDvar = MCD['returns'].var()
MCDkurt = MCD['returns'].kurt()
MCDskew = MCD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MCDbef = web.DataReader('MCD', 'yahoo', start, crashstart)
MCDbef['avgreturnbefore'] = MCDbef['Close'].pct_change(1)
MCDbef['Cumulative Return Before Crash'] = (1 + MCDbef['avgreturnbefore']).cumprod()

#period of crash
MCDdur = web.DataReader('MCD', 'yahoo', crashstart, crashend)
MCDdur['avgreturnduring'] = MCDdur['Close'].pct_change(1)
MCDdur['Cumulative Return During Crash'] = (1 + MCDdur['avgreturnduring']).cumprod()

#period after crash
MCDaft = web.DataReader('MCD', 'yahoo', crashend, end)
MCDaft['avgreturnafter'] = MCDaft['Close'].pct_change(1)
MCDaft['Cumulative Return After Crash'] = (1 + MCDaft['avgreturnafter']).cumprod()
In [284]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MGM = web.DataReader('MGM', 'yahoo', start, end)
MGM['returns'] = MGM['Close'].pct_change(1)
MGM['Cumulative Return'] = (1 + MGM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MGMmean = MGM['returns'].mean()
MGMvar = MGM['returns'].var()
MGMkurt = MGM['returns'].kurt()
MGMskew = MGM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MGMbef = web.DataReader('MGM', 'yahoo', start, crashstart)
MGMbef['avgreturnbefore'] = MGMbef['Close'].pct_change(1)
MGMbef['Cumulative Return Before Crash'] = (1 + MGMbef['avgreturnbefore']).cumprod()

#period of crash
MGMdur = web.DataReader('MGM', 'yahoo', crashstart, crashend)
MGMdur['avgreturnduring'] = MGMdur['Close'].pct_change(1)
MGMdur['Cumulative Return During Crash'] = (1 + MGMdur['avgreturnduring']).cumprod()

#period after crash
MGMaft = web.DataReader('MGM', 'yahoo', crashend, end)
MGMaft['avgreturnafter'] = MGMaft['Close'].pct_change(1)
MGMaft['Cumulative Return After Crash'] = (1 + MGMaft['avgreturnafter']).cumprod()
In [285]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MHK = web.DataReader('MHK', 'yahoo', start, end)
MHK['returns'] = MHK['Close'].pct_change(1)
MHK['Cumulative Return'] = (1 + MHK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MHKmean = MHK['returns'].mean()
MHKvar = MHK['returns'].var()
MHKkurt = MHK['returns'].kurt()
MHKskew = MHK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MHKbef = web.DataReader('MHK', 'yahoo', start, crashstart)
MHKbef['avgreturnbefore'] = MHKbef['Close'].pct_change(1)
MHKbef['Cumulative Return Before Crash'] = (1 + MHKbef['avgreturnbefore']).cumprod()

#period of crash
MHKdur = web.DataReader('MHK', 'yahoo', crashstart, crashend)
MHKdur['avgreturnduring'] = MHKdur['Close'].pct_change(1)
MHKdur['Cumulative Return During Crash'] = (1 + MHKdur['avgreturnduring']).cumprod()

#period after crash
MHKaft = web.DataReader('MHK', 'yahoo', crashend, end)
MHKaft['avgreturnafter'] = MHKaft['Close'].pct_change(1)
MHKaft['Cumulative Return After Crash'] = (1 + MHKaft['avgreturnafter']).cumprod()
In [286]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NWL = web.DataReader('NWL', 'yahoo', start, end)
NWL['returns'] = NWL['Close'].pct_change(1)
NWL['Cumulative Return'] = (1 + NWL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NWLmean = NWL['returns'].mean()
NWLvar = NWL['returns'].var()
NWLkurt = NWL['returns'].kurt()
NWLskew = NWL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NWLbef = web.DataReader('NWL', 'yahoo', start, crashstart)
NWLbef['avgreturnbefore'] = NWLbef['Close'].pct_change(1)
NWLbef['Cumulative Return Before Crash'] = (1 + NWLbef['avgreturnbefore']).cumprod()

#period of crash
NWLdur = web.DataReader('NWL', 'yahoo', crashstart, crashend)
NWLdur['avgreturnduring'] = NWLdur['Close'].pct_change(1)
NWLdur['Cumulative Return During Crash'] = (1 + NWLdur['avgreturnduring']).cumprod()

#period after crash
NWLaft = web.DataReader('NWL', 'yahoo', crashend, end)
NWLaft['avgreturnafter'] = NWLaft['Close'].pct_change(1)
NWLaft['Cumulative Return After Crash'] = (1 + NWLaft['avgreturnafter']).cumprod()
In [287]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NKE = web.DataReader('NKE', 'yahoo', start, end)
NKE['returns'] = NKE['Close'].pct_change(1)
NKE['Cumulative Return'] = (1 + NKE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NKEmean = NKE['returns'].mean()
NKEvar = NKE['returns'].var()
NKEkurt = NKE['returns'].kurt()
NKEskew = NKE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NKEbef = web.DataReader('NKE', 'yahoo', start, crashstart)
NKEbef['avgreturnbefore'] = NKEbef['Close'].pct_change(1)
NKEbef['Cumulative Return Before Crash'] = (1 + NKEbef['avgreturnbefore']).cumprod()

#period of crash
NKEdur = web.DataReader('NKE', 'yahoo', crashstart, crashend)
NKEdur['avgreturnduring'] = NKEdur['Close'].pct_change(1)
NKEdur['Cumulative Return During Crash'] = (1 + NKEdur['avgreturnduring']).cumprod()

#period after crash
NKEaft = web.DataReader('NKE', 'yahoo', crashend, end)
NKEaft['avgreturnafter'] = NKEaft['Close'].pct_change(1)
NKEaft['Cumulative Return After Crash'] = (1 + NKEaft['avgreturnafter']).cumprod()
In [288]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
JWN = web.DataReader('JWN', 'yahoo', start, end)
JWN['returns'] = JWN['Close'].pct_change(1)
JWN['Cumulative Return'] = (1 + JWN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
JWNmean = JWN['returns'].mean()
JWNvar = JWN['returns'].var()
JWNkurt = JWN['returns'].kurt()
JWNskew = JWN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
JWNbef = web.DataReader('JWN', 'yahoo', start, crashstart)
JWNbef['avgreturnbefore'] = JWNbef['Close'].pct_change(1)
JWNbef['Cumulative Return Before Crash'] = (1 + JWNbef['avgreturnbefore']).cumprod()

#period of crash
JWNdur = web.DataReader('JWN', 'yahoo', crashstart, crashend)
JWNdur['avgreturnduring'] = JWNdur['Close'].pct_change(1)
JWNdur['Cumulative Return During Crash'] = (1 + JWNdur['avgreturnduring']).cumprod()

#period after crash
JWNaft = web.DataReader('JWN', 'yahoo', crashend, end)
JWNaft['avgreturnafter'] = JWNaft['Close'].pct_change(1)
JWNaft['Cumulative Return After Crash'] = (1 + JWNaft['avgreturnafter']).cumprod()
In [289]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NCLH = web.DataReader('NCLH', 'yahoo', start, end)
NCLH['returns'] = NCLH['Close'].pct_change(1)
NCLH['Cumulative Return'] = (1 + NCLH['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NCLHmean = NCLH['returns'].mean()
NCLHvar = NCLH['returns'].var()
NCLHkurt = NCLH['returns'].kurt()
NCLHskew = NCLH['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NCLHbef = web.DataReader('NCLH', 'yahoo', start, crashstart)
NCLHbef['avgreturnbefore'] = NCLHbef['Close'].pct_change(1)
NCLHbef['Cumulative Return Before Crash'] = (1 + NCLHbef['avgreturnbefore']).cumprod()

#period of crash
NCLHdur = web.DataReader('NCLH', 'yahoo', crashstart, crashend)
NCLHdur['avgreturnduring'] = NCLHdur['Close'].pct_change(1)
NCLHdur['Cumulative Return During Crash'] = (1 + NCLHdur['avgreturnduring']).cumprod()

#period after crash
NCLHaft = web.DataReader('NCLH', 'yahoo', crashend, end)
NCLHaft['avgreturnafter'] = NCLHaft['Close'].pct_change(1)
NCLHaft['Cumulative Return After Crash'] = (1 + NCLHaft['avgreturnafter']).cumprod()
In [290]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NVR = web.DataReader('NVR', 'yahoo', start, end)
NVR['returns'] = NVR['Close'].pct_change(1)
NVR['Cumulative Return'] = (1 + NVR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NVRmean = NVR['returns'].mean()
NVRvar = NVR['returns'].var()
NVRkurt = NVR['returns'].kurt()
NVRskew = NVR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NVRbef = web.DataReader('NVR', 'yahoo', start, crashstart)
NVRbef['avgreturnbefore'] = NVRbef['Close'].pct_change(1)
NVRbef['Cumulative Return Before Crash'] = (1 + NVRbef['avgreturnbefore']).cumprod()

#period of crash
NVRdur = web.DataReader('NVR', 'yahoo', crashstart, crashend)
NVRdur['avgreturnduring'] = NVRdur['Close'].pct_change(1)
NVRdur['Cumulative Return During Crash'] = (1 + NVRdur['avgreturnduring']).cumprod()

#period after crash
NVRaft = web.DataReader('NVR', 'yahoo', crashend, end)
NVRaft['avgreturnafter'] = NVRaft['Close'].pct_change(1)
NVRaft['Cumulative Return After Crash'] = (1 + NVRaft['avgreturnafter']).cumprod()
In [291]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ORLY = web.DataReader('ORLY', 'yahoo', start, end)
ORLY['returns'] = ORLY['Close'].pct_change(1)
ORLY['Cumulative Return'] = (1 + ORLY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ORLYmean = ORLY['returns'].mean()
ORLYvar = ORLY['returns'].var()
ORLYkurt = ORLY['returns'].kurt()
ORLYskew = ORLY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ORLYbef = web.DataReader('ORLY', 'yahoo', start, crashstart)
ORLYbef['avgreturnbefore'] = ORLYbef['Close'].pct_change(1)
ORLYbef['Cumulative Return Before Crash'] = (1 + ORLYbef['avgreturnbefore']).cumprod()

#period of crash
ORLYdur = web.DataReader('ORLY', 'yahoo', crashstart, crashend)
ORLYdur['avgreturnduring'] = ORLYdur['Close'].pct_change(1)
ORLYdur['Cumulative Return During Crash'] = (1 + ORLYdur['avgreturnduring']).cumprod()

#period after crash
ORLYaft = web.DataReader('ORLY', 'yahoo', crashend, end)
ORLYaft['avgreturnafter'] = ORLYaft['Close'].pct_change(1)
ORLYaft['Cumulative Return After Crash'] = (1 + ORLYaft['avgreturnafter']).cumprod()
In [292]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PHM = web.DataReader('PHM', 'yahoo', start, end)
PHM['returns'] = PHM['Close'].pct_change(1)
PHM['Cumulative Return'] = (1 + PHM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PHMmean = PHM['returns'].mean()
PHMvar = PHM['returns'].var()
PHMkurt = PHM['returns'].kurt()
PHMskew = PHM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PHMbef = web.DataReader('PHM', 'yahoo', start, crashstart)
PHMbef['avgreturnbefore'] = PHMbef['Close'].pct_change(1)
PHMbef['Cumulative Return Before Crash'] = (1 + PHMbef['avgreturnbefore']).cumprod()

#period of crash
PHMdur = web.DataReader('PHM', 'yahoo', crashstart, crashend)
PHMdur['avgreturnduring'] = PHMdur['Close'].pct_change(1)
PHMdur['Cumulative Return During Crash'] = (1 + PHMdur['avgreturnduring']).cumprod()

#period after crash
PHMaft = web.DataReader('PHM', 'yahoo', crashend, end)
PHMaft['avgreturnafter'] = PHMaft['Close'].pct_change(1)
PHMaft['Cumulative Return After Crash'] = (1 + PHMaft['avgreturnafter']).cumprod()
In [293]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PVH = web.DataReader('PVH', 'yahoo', start, end)
PVH['returns'] = PVH['Close'].pct_change(1)
PVH['Cumulative Return'] = (1 + PVH['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PVHmean = PVH['returns'].mean()
PVHvar = PVH['returns'].var()
PVHkurt = PVH['returns'].kurt()
PVHskew = PVH['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PVHbef = web.DataReader('PVH', 'yahoo', start, crashstart)
PVHbef['avgreturnbefore'] = PVHbef['Close'].pct_change(1)
PVHbef['Cumulative Return Before Crash'] = (1 + PVHbef['avgreturnbefore']).cumprod()

#period of crash
PVHdur = web.DataReader('PVH', 'yahoo', crashstart, crashend)
PVHdur['avgreturnduring'] = PVHdur['Close'].pct_change(1)
PVHdur['Cumulative Return During Crash'] = (1 + PVHdur['avgreturnduring']).cumprod()

#period after crash
PVHaft = web.DataReader('PVH', 'yahoo', crashend, end)
PVHaft['avgreturnafter'] = PVHaft['Close'].pct_change(1)
PVHaft['Cumulative Return After Crash'] = (1 + PVHaft['avgreturnafter']).cumprod()
In [294]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
RL = web.DataReader('RL', 'yahoo', start, end)
RL['returns'] = RL['Close'].pct_change(1)
RL['Cumulative Return'] = (1 + RL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
RLmean = RL['returns'].mean()
RLvar = RL['returns'].var()
RLkurt = RL['returns'].kurt()
RLskew = RL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
RLbef = web.DataReader('RL', 'yahoo', start, crashstart)
RLbef['avgreturnbefore'] = RLbef['Close'].pct_change(1)
RLbef['Cumulative Return Before Crash'] = (1 + RLbef['avgreturnbefore']).cumprod()

#period of crash
RLdur = web.DataReader('RL', 'yahoo', crashstart, crashend)
RLdur['avgreturnduring'] = RLdur['Close'].pct_change(1)
RLdur['Cumulative Return During Crash'] = (1 + RLdur['avgreturnduring']).cumprod()

#period after crash
RLaft = web.DataReader('RL', 'yahoo', crashend, end)
RLaft['avgreturnafter'] = RLaft['Close'].pct_change(1)
RLaft['Cumulative Return After Crash'] = (1 + RLaft['avgreturnafter']).cumprod()
In [295]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ROST = web.DataReader('ROST', 'yahoo', start, end)
ROST['returns'] = ROST['Close'].pct_change(1)
ROST['Cumulative Return'] = (1 + ROST['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ROSTmean = ROST['returns'].mean()
ROSTvar = ROST['returns'].var()
ROSTkurt = ROST['returns'].kurt()
ROSTskew = ROST['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ROSTbef = web.DataReader('ROST', 'yahoo', start, crashstart)
ROSTbef['avgreturnbefore'] = ROSTbef['Close'].pct_change(1)
ROSTbef['Cumulative Return Before Crash'] = (1 + ROSTbef['avgreturnbefore']).cumprod()

#period of crash
ROSTdur = web.DataReader('ROST', 'yahoo', crashstart, crashend)
ROSTdur['avgreturnduring'] = ROSTdur['Close'].pct_change(1)
ROSTdur['Cumulative Return During Crash'] = (1 + ROSTdur['avgreturnduring']).cumprod()

#period after crash
ROSTaft = web.DataReader('ROST', 'yahoo', crashend, end)
ROSTaft['avgreturnafter'] = ROSTaft['Close'].pct_change(1)
ROSTaft['Cumulative Return After Crash'] = (1 + ROSTaft['avgreturnafter']).cumprod()
In [296]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
RCL = web.DataReader('RCL', 'yahoo', start, end)
RCL['returns'] = RCL['Close'].pct_change(1)
RCL['Cumulative Return'] = (1 + RCL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
RCLmean = RCL['returns'].mean()
RCLvar = RCL['returns'].var()
RCLkurt = RCL['returns'].kurt()
RCLskew = RCL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
RCLbef = web.DataReader('RCL', 'yahoo', start, crashstart)
RCLbef['avgreturnbefore'] = RCLbef['Close'].pct_change(1)
RCLbef['Cumulative Return Before Crash'] = (1 + RCLbef['avgreturnbefore']).cumprod()

#period of crash
RCLdur = web.DataReader('RCL', 'yahoo', crashstart, crashend)
RCLdur['avgreturnduring'] = RCLdur['Close'].pct_change(1)
RCLdur['Cumulative Return During Crash'] = (1 + RCLdur['avgreturnduring']).cumprod()

#period after crash
RCLaft = web.DataReader('RCL', 'yahoo', crashend, end)
RCLaft['avgreturnafter'] = RCLaft['Close'].pct_change(1)
RCLaft['Cumulative Return After Crash'] = (1 + RCLaft['avgreturnafter']).cumprod()
In [297]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SBUX = web.DataReader('SBUX', 'yahoo', start, end)
SBUX['returns'] = SBUX['Close'].pct_change(1)
SBUX['Cumulative Return'] = (1 + SBUX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SBUXmean = SBUX['returns'].mean()
SBUXvar = SBUX['returns'].var()
SBUXkurt = SBUX['returns'].kurt()
SBUXskew = SBUX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SBUXbef = web.DataReader('SBUX', 'yahoo', start, crashstart)
SBUXbef['avgreturnbefore'] = SBUXbef['Close'].pct_change(1)
SBUXbef['Cumulative Return Before Crash'] = (1 + SBUXbef['avgreturnbefore']).cumprod()

#period of crash
SBUXdur = web.DataReader('SBUX', 'yahoo', crashstart, crashend)
SBUXdur['avgreturnduring'] = SBUXdur['Close'].pct_change(1)
SBUXdur['Cumulative Return During Crash'] = (1 + SBUXdur['avgreturnduring']).cumprod()

#period after crash
SBUXaft = web.DataReader('SBUX', 'yahoo', crashend, end)
SBUXaft['avgreturnafter'] = SBUXaft['Close'].pct_change(1)
SBUXaft['Cumulative Return After Crash'] = (1 + SBUXaft['avgreturnafter']).cumprod()
In [298]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TPR = web.DataReader('TPR', 'yahoo', start, end)
TPR['returns'] = TPR['Close'].pct_change(1)
TPR['Cumulative Return'] = (1 + TPR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TPRmean = TPR['returns'].mean()
TPRvar = TPR['returns'].var()
TPRkurt = TPR['returns'].kurt()
TPRskew = TPR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TPRbef = web.DataReader('TPR', 'yahoo', start, crashstart)
TPRbef['avgreturnbefore'] = TPRbef['Close'].pct_change(1)
TPRbef['Cumulative Return Before Crash'] = (1 + TPRbef['avgreturnbefore']).cumprod()

#period of crash
TPRdur = web.DataReader('TPR', 'yahoo', crashstart, crashend)
TPRdur['avgreturnduring'] = TPRdur['Close'].pct_change(1)
TPRdur['Cumulative Return During Crash'] = (1 + TPRdur['avgreturnduring']).cumprod()

#period after crash
TPRaft = web.DataReader('TPR', 'yahoo', crashend, end)
TPRaft['avgreturnafter'] = TPRaft['Close'].pct_change(1)
TPRaft['Cumulative Return After Crash'] = (1 + TPRaft['avgreturnafter']).cumprod()
In [299]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TGT = web.DataReader('TGT', 'yahoo', start, end)
TGT['returns'] = TGT['Close'].pct_change(1)
TGT['Cumulative Return'] = (1 + TGT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TGTmean = TGT['returns'].mean()
TGTvar = TGT['returns'].var()
TGTkurt = TGT['returns'].kurt()
TGTskew = TGT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TGTbef = web.DataReader('TGT', 'yahoo', start, crashstart)
TGTbef['avgreturnbefore'] = TGTbef['Close'].pct_change(1)
TGTbef['Cumulative Return Before Crash'] = (1 + TGTbef['avgreturnbefore']).cumprod()

#period of crash
TGTdur = web.DataReader('TGT', 'yahoo', crashstart, crashend)
TGTdur['avgreturnduring'] = TGTdur['Close'].pct_change(1)
TGTdur['Cumulative Return During Crash'] = (1 + TGTdur['avgreturnduring']).cumprod()

#period after crash
TGTaft = web.DataReader('TGT', 'yahoo', crashend, end)
TGTaft['avgreturnafter'] = TGTaft['Close'].pct_change(1)
TGTaft['Cumulative Return After Crash'] = (1 + TGTaft['avgreturnafter']).cumprod()
In [300]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TIF = web.DataReader('TIF', 'yahoo', start, end)
TIF['returns'] = TIF['Close'].pct_change(1)
TIF['Cumulative Return'] = (1 + TIF['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TIFmean = TIF['returns'].mean()
TIFvar = TIF['returns'].var()
TIFkurt = TIF['returns'].kurt()
TIFskew = TIF['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TIFbef = web.DataReader('TIF', 'yahoo', start, crashstart)
TIFbef['avgreturnbefore'] = TIFbef['Close'].pct_change(1)
TIFbef['Cumulative Return Before Crash'] = (1 + TIFbef['avgreturnbefore']).cumprod()

#period of crash
TIFdur = web.DataReader('TIF', 'yahoo', crashstart, crashend)
TIFdur['avgreturnduring'] = TIFdur['Close'].pct_change(1)
TIFdur['Cumulative Return During Crash'] = (1 + TIFdur['avgreturnduring']).cumprod()

#period after crash
TIFaft = web.DataReader('TIF', 'yahoo', crashend, end)
TIFaft['avgreturnafter'] = TIFaft['Close'].pct_change(1)
TIFaft['Cumulative Return After Crash'] = (1 + TIFaft['avgreturnafter']).cumprod()
In [301]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TJX = web.DataReader('TJX', 'yahoo', start, end)
TJX['returns'] = TJX['Close'].pct_change(1)
TJX['Cumulative Return'] = (1 + TJX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TJXmean = TJX['returns'].mean()
TJXvar = TJX['returns'].var()
TJXkurt = TJX['returns'].kurt()
TJXskew = TJX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TJXbef = web.DataReader('TJX', 'yahoo', start, crashstart)
TJXbef['avgreturnbefore'] = TJXbef['Close'].pct_change(1)
TJXbef['Cumulative Return Before Crash'] = (1 + TJXbef['avgreturnbefore']).cumprod()

#period of crash
TJXdur = web.DataReader('TJX', 'yahoo', crashstart, crashend)
TJXdur['avgreturnduring'] = TJXdur['Close'].pct_change(1)
TJXdur['Cumulative Return During Crash'] = (1 + TJXdur['avgreturnduring']).cumprod()

#period after crash
TJXaft = web.DataReader('TJX', 'yahoo', crashend, end)
TJXaft['avgreturnafter'] = TJXaft['Close'].pct_change(1)
TJXaft['Cumulative Return After Crash'] = (1 + TJXaft['avgreturnafter']).cumprod()
In [302]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TSCO = web.DataReader('TSCO', 'yahoo', start, end)
TSCO['returns'] = TSCO['Close'].pct_change(1)
TSCO['Cumulative Return'] = (1 + TSCO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TSCOmean = TSCO['returns'].mean()
TSCOvar = TSCO['returns'].var()
TSCOkurt = TSCO['returns'].kurt()
TSCOskew = TSCO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TSCObef = web.DataReader('TSCO', 'yahoo', start, crashstart)
TSCObef['avgreturnbefore'] = TSCObef['Close'].pct_change(1)
TSCObef['Cumulative Return Before Crash'] = (1 + TSCObef['avgreturnbefore']).cumprod()

#period of crash
TSCOdur = web.DataReader('TSCO', 'yahoo', crashstart, crashend)
TSCOdur['avgreturnduring'] = TSCOdur['Close'].pct_change(1)
TSCOdur['Cumulative Return During Crash'] = (1 + TSCOdur['avgreturnduring']).cumprod()

#period after crash
TSCOaft = web.DataReader('TSCO', 'yahoo', crashend, end)
TSCOaft['avgreturnafter'] = TSCOaft['Close'].pct_change(1)
TSCOaft['Cumulative Return After Crash'] = (1 + TSCOaft['avgreturnafter']).cumprod()
In [303]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ULTA = web.DataReader('ULTA', 'yahoo', start, end)
ULTA['returns'] = ULTA['Close'].pct_change(1)
ULTA['Cumulative Return'] = (1 + ULTA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ULTAmean = ULTA['returns'].mean()
ULTAvar = ULTA['returns'].var()
ULTAkurt = ULTA['returns'].kurt()
ULTAskew = ULTA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ULTAbef = web.DataReader('ULTA', 'yahoo', start, crashstart)
ULTAbef['avgreturnbefore'] = ULTAbef['Close'].pct_change(1)
ULTAbef['Cumulative Return Before Crash'] = (1 + ULTAbef['avgreturnbefore']).cumprod()

#period of crash
ULTAdur = web.DataReader('ULTA', 'yahoo', crashstart, crashend)
ULTAdur['avgreturnduring'] = ULTAdur['Close'].pct_change(1)
ULTAdur['Cumulative Return During Crash'] = (1 + ULTAdur['avgreturnduring']).cumprod()

#period after crash
ULTAaft = web.DataReader('ULTA', 'yahoo', crashend, end)
ULTAaft['avgreturnafter'] = ULTAaft['Close'].pct_change(1)
ULTAaft['Cumulative Return After Crash'] = (1 + ULTAaft['avgreturnafter']).cumprod()
In [304]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
UAA = web.DataReader('UAA', 'yahoo', start, end)
UAA['returns'] = UAA['Close'].pct_change(1)
UAA['Cumulative Return'] = (1 + UAA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
UAAmean = UAA['returns'].mean()
UAAvar = UAA['returns'].var()
UAAkurt = UAA['returns'].kurt()
UAAskew = UAA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
UAAbef = web.DataReader('UAA', 'yahoo', start, crashstart)
UAAbef['avgreturnbefore'] = UAAbef['Close'].pct_change(1)
UAAbef['Cumulative Return Before Crash'] = (1 + UAAbef['avgreturnbefore']).cumprod()

#period of crash
UAAdur = web.DataReader('UAA', 'yahoo', crashstart, crashend)
UAAdur['avgreturnduring'] = UAAdur['Close'].pct_change(1)
UAAdur['Cumulative Return During Crash'] = (1 + UAAdur['avgreturnduring']).cumprod()

#period after crash
UAAaft = web.DataReader('UAA', 'yahoo', crashend, end)
UAAaft['avgreturnafter'] = UAAaft['Close'].pct_change(1)
UAAaft['Cumulative Return After Crash'] = (1 + UAAaft['avgreturnafter']).cumprod()
In [305]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
UA = web.DataReader('UA', 'yahoo', start, end)
UA['returns'] = UA['Close'].pct_change(1)
UA['Cumulative Return'] = (1 + UA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
UAmean = UA['returns'].mean()
UAvar = UA['returns'].var()
UAkurt = UA['returns'].kurt()
UAskew = UA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
UAbef = web.DataReader('UA', 'yahoo', start, crashstart)
UAbef['avgreturnbefore'] = UAbef['Close'].pct_change(1)
UAbef['Cumulative Return Before Crash'] = (1 + UAbef['avgreturnbefore']).cumprod()

#period of crash
UAdur = web.DataReader('UA', 'yahoo', crashstart, crashend)
UAdur['avgreturnduring'] = UAdur['Close'].pct_change(1)
UAdur['Cumulative Return During Crash'] = (1 + UAdur['avgreturnduring']).cumprod()

#period after crash
UAaft = web.DataReader('UA', 'yahoo', crashend, end)
UAaft['avgreturnafter'] = UAaft['Close'].pct_change(1)
UAaft['Cumulative Return After Crash'] = (1 + UAaft['avgreturnafter']).cumprod()
In [306]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
VFC = web.DataReader('VFC', 'yahoo', start, end)
VFC['returns'] = VFC['Close'].pct_change(1)
VFC['Cumulative Return'] = (1 + VFC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
VFCmean = VFC['returns'].mean()
VFCvar = VFC['returns'].var()
VFCkurt = VFC['returns'].kurt()
VFCskew = VFC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
VFCbef = web.DataReader('VFC', 'yahoo', start, crashstart)
VFCbef['avgreturnbefore'] = VFCbef['Close'].pct_change(1)
VFCbef['Cumulative Return Before Crash'] = (1 + VFCbef['avgreturnbefore']).cumprod()

#period of crash
VFCdur = web.DataReader('VFC', 'yahoo', crashstart, crashend)
VFCdur['avgreturnduring'] = VFCdur['Close'].pct_change(1)
VFCdur['Cumulative Return During Crash'] = (1 + VFCdur['avgreturnduring']).cumprod()

#period after crash
VFCaft = web.DataReader('VFC', 'yahoo', crashend, end)
VFCaft['avgreturnafter'] = VFCaft['Close'].pct_change(1)
VFCaft['Cumulative Return After Crash'] = (1 + VFCaft['avgreturnafter']).cumprod()
In [307]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WHR = web.DataReader('WHR', 'yahoo', start, end)
WHR['returns'] = WHR['Close'].pct_change(1)
WHR['Cumulative Return'] = (1 + WHR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WHRmean = WHR['returns'].mean()
WHRvar = WHR['returns'].var()
WHRkurt = WHR['returns'].kurt()
WHRskew = WHR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WHRbef = web.DataReader('WHR', 'yahoo', start, crashstart)
WHRbef['avgreturnbefore'] = WHRbef['Close'].pct_change(1)
WHRbef['Cumulative Return Before Crash'] = (1 + WHRbef['avgreturnbefore']).cumprod()

#period of crash
WHRdur = web.DataReader('WHR', 'yahoo', crashstart, crashend)
WHRdur['avgreturnduring'] = WHRdur['Close'].pct_change(1)
WHRdur['Cumulative Return During Crash'] = (1 + WHRdur['avgreturnduring']).cumprod()

#period after crash
WHRaft = web.DataReader('WHR', 'yahoo', crashend, end)
WHRaft['avgreturnafter'] = WHRaft['Close'].pct_change(1)
WHRaft['Cumulative Return After Crash'] = (1 + WHRaft['avgreturnafter']).cumprod()
In [308]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WYNN = web.DataReader('WYNN', 'yahoo', start, end)
WYNN['returns'] = WYNN['Close'].pct_change(1)
WYNN['Cumulative Return'] = (1 + WYNN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WYNNmean = WYNN['returns'].mean()
WYNNvar = WYNN['returns'].var()
WYNNkurt = WYNN['returns'].kurt()
WYNNskew = WYNN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WYNNbef = web.DataReader('WYNN', 'yahoo', start, crashstart)
WYNNbef['avgreturnbefore'] = WYNNbef['Close'].pct_change(1)
WYNNbef['Cumulative Return Before Crash'] = (1 + WYNNbef['avgreturnbefore']).cumprod()

#period of crash
WYNNdur = web.DataReader('WYNN', 'yahoo', crashstart, crashend)
WYNNdur['avgreturnduring'] = WYNNdur['Close'].pct_change(1)
WYNNdur['Cumulative Return During Crash'] = (1 + WYNNdur['avgreturnduring']).cumprod()

#period after crash
WYNNaft = web.DataReader('WYNN', 'yahoo', crashend, end)
WYNNaft['avgreturnafter'] = WYNNaft['Close'].pct_change(1)
WYNNaft['Cumulative Return After Crash'] = (1 + WYNNaft['avgreturnafter']).cumprod()
In [309]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
YUM = web.DataReader('YUM', 'yahoo', start, end)
YUM['returns'] = YUM['Close'].pct_change(1)
YUM['Cumulative Return'] = (1 + YUM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
YUMmean = YUM['returns'].mean()
YUMvar = YUM['returns'].var()
YUMkurt = YUM['returns'].kurt()
YUMskew = YUM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
YUMbef = web.DataReader('YUM', 'yahoo', start, crashstart)
YUMbef['avgreturnbefore'] = YUMbef['Close'].pct_change(1)
YUMbef['Cumulative Return Before Crash'] = (1 + YUMbef['avgreturnbefore']).cumprod()

#period of crash
YUMdur = web.DataReader('YUM', 'yahoo', crashstart, crashend)
YUMdur['avgreturnduring'] = YUMdur['Close'].pct_change(1)
YUMdur['Cumulative Return During Crash'] = (1 + YUMdur['avgreturnduring']).cumprod()

#period after crash
YUMaft = web.DataReader('YUM', 'yahoo', crashend, end)
YUMaft['avgreturnafter'] = YUMaft['Close'].pct_change(1)
YUMaft['Cumulative Return After Crash'] = (1 + YUMaft['avgreturnafter']).cumprod()
In [310]:
#KDE Plot for Consumer Discretionary Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
AAP['returns'].plot(kind = 'kde', label ='AAP', figsize = (10,8), alpha=0.5, title = 'KDE of Consumer Discretionary Stocks')
AMZN['returns'].plot(kind = 'kde', label ='AMZN', alpha=0.5)
APTV['returns'].plot(kind = 'kde', label ='APTV', alpha=0.5)
AZO['returns'].plot(kind = 'kde', label ='AZO', alpha=0.5)
BBY['returns'].plot(kind = 'kde', label ='BBY', alpha=0.5)
BKNG['returns'].plot(kind = 'kde', label ='BKNG', alpha=0.5)
BWA['returns'].plot(kind = 'kde', label ='BWA', alpha=0.5)
KMX['returns'].plot(kind = 'kde', label ='KMX', alpha=0.5)
CCL['returns'].plot(kind = 'kde', label ='CCL', alpha=0.5)
CMG['returns'].plot(kind = 'kde', label ='CMG', alpha=0.5)
DHI['returns'].plot(kind = 'kde', label ='DHI', alpha=0.5)
DRI['returns'].plot(kind = 'kde', label ='DRI', alpha=0.5)
DG['returns'].plot(kind = 'kde', label ='DG', alpha=0.5)
DLTR['returns'].plot(kind = 'kde', label ='DLTR', alpha=0.5)
DPZ['returns'].plot(kind = 'kde', label = 'DPZ', alpha=0.5)
EBAY['returns'].plot(kind = 'kde', label ='EBAY', alpha=0.5)
EXPE['returns'].plot(kind = 'kde', label ='EXPE', alpha=0.5)
F['returns'].plot(kind = 'kde', label ='F', alpha=0.5)
GPS['returns'].plot(kind = 'kde', label ='GPS', alpha=0.5)
GRMN['returns'].plot(kind = 'kde', label ='GRMN', alpha=0.5)
GM['returns'].plot(kind = 'kde', label ='GM', alpha=0.5)
GPC['returns'].plot(kind = 'kde', label ='GPC', alpha=0.5)
HRB['returns'].plot(kind = 'kde', label ='HRB', alpha=0.5)
HBI['returns'].plot(kind = 'kde', label ='HBI', alpha=0.5)
HOG['returns'].plot(kind = 'kde', label ='HOG', alpha=0.5)
HAS['returns'].plot(kind = 'kde', label ='HAS', alpha=0.5)
HLT['returns'].plot(kind = 'kde', label ='HLT', alpha=0.5)
HD['returns'].plot(kind = 'kde', label ='HD', alpha=0.5)
KSS['returns'].plot(kind = 'kde', label ='KSS', alpha=0.5)
LB['returns'].plot(kind = 'kde', label ='LB', alpha=0.5)
LVS['returns'].plot(kind = 'kde', label ='LVS', alpha=0.5)
LEG['returns'].plot(kind = 'kde', label ='LEG', alpha=0.5)
LEN['returns'].plot(kind = 'kde', label ='LEN', alpha=0.5)
LKQ['returns'].plot(kind = 'kde', label ='LKQ', alpha=0.5)
LOW['returns'].plot(kind = 'kde', label ='LOW', alpha=0.5)
MAR['returns'].plot(kind = 'kde', label ='MAR', alpha=0.5)
MCD['returns'].plot(kind = 'kde', label ='MCD', alpha=0.5)
MGM['returns'].plot(kind = 'kde', label ='MGM', alpha=0.5)
MHK['returns'].plot(kind = 'kde', label ='MHK', alpha=0.5)
NWL['returns'].plot(kind = 'kde', label ='NWL', alpha=0.5)
NKE['returns'].plot(kind = 'kde', label ='NKE', alpha=0.5)
JWN['returns'].plot(kind = 'kde', label ='JWN', alpha=0.5)
NCLH['returns'].plot(kind = 'kde', label ='NCLH', alpha=0.5)
NVR['returns'].plot(kind = 'kde', label ='NVR', alpha=0.5)
ORLY['returns'].plot(kind = 'kde', label ='ORLY', alpha=0.5)
PHM['returns'].plot(kind = 'kde', label ='PHM', alpha=0.5)
PVH['returns'].plot(kind = 'kde', label ='PVH', alpha=0.5)
RL['returns'].plot(kind = 'kde', label ='RL', alpha=0.5)
ROST['returns'].plot(kind = 'kde', label ='ROST', alpha=0.5)
RCL['returns'].plot(kind = 'kde', label ='RCL', alpha=0.5)
SBUX['returns'].plot(kind = 'kde', label ='SBUX', alpha=0.5)
TPR['returns'].plot(kind = 'kde', label ='TPR', alpha=0.5)
TGT['returns'].plot(kind = 'kde', label ='TGT', alpha=0.5)
TIF['returns'].plot(kind = 'kde', label ='TIF', alpha=0.5)
TJX['returns'].plot(kind = 'kde', label ='TJX', alpha=0.5)
TSCO['returns'].plot(kind = 'kde', label ='TSCO', alpha=0.5)
ULTA['returns'].plot(kind = 'kde', label ='ULTA', alpha=0.5)
UAA['returns'].plot(kind = 'kde', label ='UAA', alpha=0.5)
UA['returns'].plot(kind = 'kde', label ='UA', alpha=0.5)
VFC['returns'].plot(kind = 'kde', label ='VFC', alpha=0.5)
WHR['returns'].plot(kind = 'kde', label ='WHR', alpha=0.5)
WYNN['returns'].plot(kind = 'kde', label ='WYNN', alpha=0.5)
YUM['returns'].plot(kind = 'kde', label ='YUM', alpha=0.5)

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [311]:
#Boxplots comparing returns for Consumer Discretionary Sector
box_df = pd.concat([sp['returns'],AAP['returns'],AMZN['returns'],APTV['returns'],AZO['returns'],BBY['returns'],BKNG['returns'],BWA['returns'],KMX['returns'],CCL['returns'],CMG['returns'],DHI['returns'],DRI['returns'],DG['returns'],DLTR['returns'],DPZ['returns'],EBAY['returns'],EXPE['returns'],F['returns'],GPS['returns'],GRMN['returns'],GM['returns'],GPC['returns'],HRB['returns'],HBI['returns'],HOG['returns'],HAS['returns'],HLT['returns'],HD['returns'],KSS['returns'],LB['returns'],LVS['returns'],LEG['returns'],LEN['returns'],LKQ['returns'],LOW['returns'],MAR['returns'],MCD['returns'],MGM['returns'],MHK['returns'],NWL['returns'],NKE['returns'],JWN['returns'],NCLH['returns'],NVR['returns'],ORLY['returns'],PHM['returns'],PVH['returns'],RL['returns'],ROST['returns'],RCL['returns'],SBUX['returns'],TPR['returns'],TGT['returns'],TIF['returns'],TJX['returns'],TSCO['returns'],ULTA['returns'],UAA['returns'],UA['returns'],VFC['returns'],WHR['returns'],WYNN['returns'],YUM['returns']], axis=1)
box_df.columns = ['S&P 500','AAP','AMZN','APTV','AZO','BBY','BKNG','BWA','KMX','CCL','CMG','DHI','DRI','DG','DLTR','DPZ','EBAY','EXPE','F','GPS','GRMN','GM','GPC','HRB','HBI','HOG','HAS','HLT','HD','KSS','LB','LVS','LEG','LEN','LKQ','LOW','MAR','MCD','MGM','MHK','NWL','NKE','JWN','NCLH','NVR','ORLY','PHM','PVH','RL','ROST','RCL','SBUX','TPR','TGT','TIF','TJX','TSCO','ULTA','UAA','UA','VFC','WHR','WYNN','YUM']
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Consumer Discretionary Sector Returns')
Out[311]:
<matplotlib.axes._subplots.AxesSubplot at 0x13f9e7d4eb8>
In [312]:
#Cumulative Returns of Consumer Discretionary Stocks for Whole period

sp['Cumulative Return'].plot(label="S&P 500")
AAP['Cumulative Return'].plot(label='AAP', figsize=(16,8), title = 'Cumulative Return of Consumer Discretionary Stocks')
AMZN['Cumulative Return'].plot(label="AMZN")
APTV['Cumulative Return'].plot(label="APTV")
AZO['Cumulative Return'].plot(label="AZO")
BBY['Cumulative Return'].plot(label="BBY")
BKNG['Cumulative Return'].plot(label="BKNG")
BWA['Cumulative Return'].plot(label="BWA")
KMX['Cumulative Return'].plot(label="KMX")
CCL['Cumulative Return'].plot(label="CCL")
CMG['Cumulative Return'].plot(label="CMG")
DHI['Cumulative Return'].plot(label="DHI")
DRI['Cumulative Return'].plot(label="DRI")
DG['Cumulative Return'].plot(label="DG")
DLTR['Cumulative Return'].plot(label="DLTR")
DPZ['Cumulative Return'].plot(label="DPZ")
EBAY['Cumulative Return'].plot(label="EBAY")
EXPE['Cumulative Return'].plot(label="EXPE")
F['Cumulative Return'].plot(label="F")
GPS['Cumulative Return'].plot(label="GPS")
GRMN['Cumulative Return'].plot(label="GRMN")
GM['Cumulative Return'].plot(label="GM")
GPC['Cumulative Return'].plot(label="GPC")
HRB['Cumulative Return'].plot(label="HRB")
HBI['Cumulative Return'].plot(label="HBI")
HOG['Cumulative Return'].plot(label="HOG")
HAS['Cumulative Return'].plot(label="HAS")
HLT['Cumulative Return'].plot(label="HLT")
HD['Cumulative Return'].plot(label="HD")
KSS['Cumulative Return'].plot(label="KSS")
LB['Cumulative Return'].plot(label="LB")
LVS['Cumulative Return'].plot(label="LVS")
LEG['Cumulative Return'].plot(label="LEG")
LEN['Cumulative Return'].plot(label="LEN")
LKQ['Cumulative Return'].plot(label="LKQ")
LOW['Cumulative Return'].plot(label="LOW")
MAR['Cumulative Return'].plot(label="MAR")
MCD['Cumulative Return'].plot(label="MCD")
MGM['Cumulative Return'].plot(label="MGM")
MHK['Cumulative Return'].plot(label="MHK")
NWL['Cumulative Return'].plot(label="NWL")
NKE['Cumulative Return'].plot(label="NKE")
JWN['Cumulative Return'].plot(label="JWN")
NCLH['Cumulative Return'].plot(label="NCLH")
NVR['Cumulative Return'].plot(label="NVR")
ORLY['Cumulative Return'].plot(label="ORLY")
PHM['Cumulative Return'].plot(label="PHM")
PVH['Cumulative Return'].plot(label="PVH")
RL['Cumulative Return'].plot(label="RL")
ROST['Cumulative Return'].plot(label="ROST")
RCL['Cumulative Return'].plot(label="RCL")
SBUX['Cumulative Return'].plot(label="SBUX")
TPR['Cumulative Return'].plot(label="TPR")
TGT['Cumulative Return'].plot(label="TGT")
TIF['Cumulative Return'].plot(label="TIF")
TJX['Cumulative Return'].plot(label="TJX")
TSCO['Cumulative Return'].plot(label="TSCO")
ULTA['Cumulative Return'].plot(label="ULTA")
UAA['Cumulative Return'].plot(label="UAA")
UA['Cumulative Return'].plot(label="UA")
VFC['Cumulative Return'].plot(label="VFC")
WHR['Cumulative Return'].plot(label="WHR")
WYNN['Cumulative Return'].plot(label="WYNN")
YUM['Cumulative Return'].plot(label="YUM")

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [313]:
#Sorting by Cumulative Return

cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],AAP['Cumulative Return'].iloc[-1],AMZN['Cumulative Return'].iloc[-1],APTV['Cumulative Return'].iloc[-1],AZO['Cumulative Return'].iloc[-1],BBY['Cumulative Return'].iloc[-1],BKNG['Cumulative Return'].iloc[-1],BWA['Cumulative Return'].iloc[-1],KMX['Cumulative Return'].iloc[-1],CCL['Cumulative Return'].iloc[-1],CMG['Cumulative Return'].iloc[-1],DHI['Cumulative Return'].iloc[-1],DRI['Cumulative Return'].iloc[-1],DG['Cumulative Return'].iloc[-1],DLTR['Cumulative Return'].iloc[-1],DPZ['Cumulative Return'].iloc[-1],EBAY['Cumulative Return'].iloc[-1],EXPE['Cumulative Return'].iloc[-1],F['Cumulative Return'].iloc[-1],GPS['Cumulative Return'].iloc[-1],GRMN['Cumulative Return'].iloc[-1],GM['Cumulative Return'].iloc[-1],GPC['Cumulative Return'].iloc[-1],HRB['Cumulative Return'].iloc[-1],HBI['Cumulative Return'].iloc[-1],HOG['Cumulative Return'].iloc[-1],HAS['Cumulative Return'].iloc[-1],HLT['Cumulative Return'].iloc[-1],HD['Cumulative Return'].iloc[-1],KSS['Cumulative Return'].iloc[-1],LB['Cumulative Return'].iloc[-1],LVS['Cumulative Return'].iloc[-1],LEG['Cumulative Return'].iloc[-1],LEN['Cumulative Return'].iloc[-1],LKQ['Cumulative Return'].iloc[-1],LOW['Cumulative Return'].iloc[-1],MAR['Cumulative Return'].iloc[-1],MCD['Cumulative Return'].iloc[-1],MGM['Cumulative Return'].iloc[-1],MHK['Cumulative Return'].iloc[-1],NWL['Cumulative Return'].iloc[-1],NKE['Cumulative Return'].iloc[-1],JWN['Cumulative Return'].iloc[-1],NCLH['Cumulative Return'].iloc[-1],NVR['Cumulative Return'].iloc[-1],ORLY['Cumulative Return'].iloc[-1],PHM['Cumulative Return'].iloc[-1],PVH['Cumulative Return'].iloc[-1],RL['Cumulative Return'].iloc[-1],ROST['Cumulative Return'].iloc[-1],RCL['Cumulative Return'].iloc[-1],SBUX['Cumulative Return'].iloc[-1],TPR['Cumulative Return'].iloc[-1],TGT['Cumulative Return'].iloc[-1],TIF['Cumulative Return'].iloc[-1],TJX['Cumulative Return'].iloc[-1],TSCO['Cumulative Return'].iloc[-1],ULTA['Cumulative Return'].iloc[-1],UAA['Cumulative Return'].iloc[-1],UA['Cumulative Return'].iloc[-1],VFC['Cumulative Return'].iloc[-1],WHR['Cumulative Return'].iloc[-1],WYNN['Cumulative Return'].iloc[-1],YUM['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],AAPbef['Cumulative Return Before Crash'].iloc[-1],AMZNbef['Cumulative Return Before Crash'].iloc[-1],APTVbef['Cumulative Return Before Crash'].iloc[-1],AZObef['Cumulative Return Before Crash'].iloc[-1],BBYbef['Cumulative Return Before Crash'].iloc[-1],BKNGbef['Cumulative Return Before Crash'].iloc[-1],BWAbef['Cumulative Return Before Crash'].iloc[-1],KMXbef['Cumulative Return Before Crash'].iloc[-1],CCLbef['Cumulative Return Before Crash'].iloc[-1],CMGbef['Cumulative Return Before Crash'].iloc[-1],DHIbef['Cumulative Return Before Crash'].iloc[-1],DRIbef['Cumulative Return Before Crash'].iloc[-1],DGbef['Cumulative Return Before Crash'].iloc[-1],DLTRbef['Cumulative Return Before Crash'].iloc[-1],DPZbef['Cumulative Return Before Crash'].iloc[-1],EBAYbef['Cumulative Return Before Crash'].iloc[-1],EXPEbef['Cumulative Return Before Crash'].iloc[-1],Fbef['Cumulative Return Before Crash'].iloc[-1],GPSbef['Cumulative Return Before Crash'].iloc[-1],GRMNbef['Cumulative Return Before Crash'].iloc[-1],GMbef['Cumulative Return Before Crash'].iloc[-1],GPCbef['Cumulative Return Before Crash'].iloc[-1],HRBbef['Cumulative Return Before Crash'].iloc[-1],HBIbef['Cumulative Return Before Crash'].iloc[-1],HOGbef['Cumulative Return Before Crash'].iloc[-1],HASbef['Cumulative Return Before Crash'].iloc[-1],HLTbef['Cumulative Return Before Crash'].iloc[-1],HDbef['Cumulative Return Before Crash'].iloc[-1],KSSbef['Cumulative Return Before Crash'].iloc[-1],LBbef['Cumulative Return Before Crash'].iloc[-1],LVSbef['Cumulative Return Before Crash'].iloc[-1],LEGbef['Cumulative Return Before Crash'].iloc[-1],LENbef['Cumulative Return Before Crash'].iloc[-1],LKQbef['Cumulative Return Before Crash'].iloc[-1],LOWbef['Cumulative Return Before Crash'].iloc[-1],MARbef['Cumulative Return Before Crash'].iloc[-1],MCDbef['Cumulative Return Before Crash'].iloc[-1],MGMbef['Cumulative Return Before Crash'].iloc[-1],MHKbef['Cumulative Return Before Crash'].iloc[-1],NWLbef['Cumulative Return Before Crash'].iloc[-1],NKEbef['Cumulative Return Before Crash'].iloc[-1],JWNbef['Cumulative Return Before Crash'].iloc[-1],NCLHbef['Cumulative Return Before Crash'].iloc[-1],NVRbef['Cumulative Return Before Crash'].iloc[-1],ORLYbef['Cumulative Return Before Crash'].iloc[-1],PHMbef['Cumulative Return Before Crash'].iloc[-1],PVHbef['Cumulative Return Before Crash'].iloc[-1],RLbef['Cumulative Return Before Crash'].iloc[-1],ROSTbef['Cumulative Return Before Crash'].iloc[-1],RCLbef['Cumulative Return Before Crash'].iloc[-1],SBUXbef['Cumulative Return Before Crash'].iloc[-1],TPRbef['Cumulative Return Before Crash'].iloc[-1],TGTbef['Cumulative Return Before Crash'].iloc[-1],TIFbef['Cumulative Return Before Crash'].iloc[-1],TJXbef['Cumulative Return Before Crash'].iloc[-1],TSCObef['Cumulative Return Before Crash'].iloc[-1],ULTAbef['Cumulative Return Before Crash'].iloc[-1],UAAbef['Cumulative Return Before Crash'].iloc[-1],UAbef['Cumulative Return Before Crash'].iloc[-1],VFCbef['Cumulative Return Before Crash'].iloc[-1],WHRbef['Cumulative Return Before Crash'].iloc[-1],WYNNbef['Cumulative Return Before Crash'].iloc[-1],YUMbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],AAPdur['Cumulative Return During Crash'].iloc[-1],AMZNdur['Cumulative Return During Crash'].iloc[-1],APTVdur['Cumulative Return During Crash'].iloc[-1],AZOdur['Cumulative Return During Crash'].iloc[-1],BBYdur['Cumulative Return During Crash'].iloc[-1],BKNGdur['Cumulative Return During Crash'].iloc[-1],BWAdur['Cumulative Return During Crash'].iloc[-1],KMXdur['Cumulative Return During Crash'].iloc[-1],CCLdur['Cumulative Return During Crash'].iloc[-1],CMGdur['Cumulative Return During Crash'].iloc[-1],DHIdur['Cumulative Return During Crash'].iloc[-1],DRIdur['Cumulative Return During Crash'].iloc[-1],DGdur['Cumulative Return During Crash'].iloc[-1],DLTRdur['Cumulative Return During Crash'].iloc[-1],DPZdur['Cumulative Return During Crash'].iloc[-1],EBAYdur['Cumulative Return During Crash'].iloc[-1],EXPEdur['Cumulative Return During Crash'].iloc[-1],Fdur['Cumulative Return During Crash'].iloc[-1],GPSdur['Cumulative Return During Crash'].iloc[-1],GRMNdur['Cumulative Return During Crash'].iloc[-1],GMdur['Cumulative Return During Crash'].iloc[-1],GPCdur['Cumulative Return During Crash'].iloc[-1],HRBdur['Cumulative Return During Crash'].iloc[-1],HBIdur['Cumulative Return During Crash'].iloc[-1],HOGdur['Cumulative Return During Crash'].iloc[-1],HASdur['Cumulative Return During Crash'].iloc[-1],HLTdur['Cumulative Return During Crash'].iloc[-1],HDdur['Cumulative Return During Crash'].iloc[-1],KSSdur['Cumulative Return During Crash'].iloc[-1],LBdur['Cumulative Return During Crash'].iloc[-1],LVSdur['Cumulative Return During Crash'].iloc[-1],LEGdur['Cumulative Return During Crash'].iloc[-1],LENdur['Cumulative Return During Crash'].iloc[-1],LKQdur['Cumulative Return During Crash'].iloc[-1],LOWdur['Cumulative Return During Crash'].iloc[-1],MARdur['Cumulative Return During Crash'].iloc[-1],MCDdur['Cumulative Return During Crash'].iloc[-1],MGMdur['Cumulative Return During Crash'].iloc[-1],MHKdur['Cumulative Return During Crash'].iloc[-1],NWLdur['Cumulative Return During Crash'].iloc[-1],NKEdur['Cumulative Return During Crash'].iloc[-1],JWNdur['Cumulative Return During Crash'].iloc[-1],NCLHdur['Cumulative Return During Crash'].iloc[-1],NVRdur['Cumulative Return During Crash'].iloc[-1],ORLYdur['Cumulative Return During Crash'].iloc[-1],PHMdur['Cumulative Return During Crash'].iloc[-1],PVHdur['Cumulative Return During Crash'].iloc[-1],RLdur['Cumulative Return During Crash'].iloc[-1],ROSTdur['Cumulative Return During Crash'].iloc[-1],RCLdur['Cumulative Return During Crash'].iloc[-1],SBUXdur['Cumulative Return During Crash'].iloc[-1],TPRdur['Cumulative Return During Crash'].iloc[-1],TGTdur['Cumulative Return During Crash'].iloc[-1],TIFdur['Cumulative Return During Crash'].iloc[-1],TJXdur['Cumulative Return During Crash'].iloc[-1],TSCOdur['Cumulative Return During Crash'].iloc[-1],ULTAdur['Cumulative Return During Crash'].iloc[-1],UAAdur['Cumulative Return During Crash'].iloc[-1],UAdur['Cumulative Return During Crash'].iloc[-1],VFCdur['Cumulative Return During Crash'].iloc[-1],WHRdur['Cumulative Return During Crash'].iloc[-1],WYNNdur['Cumulative Return During Crash'].iloc[-1],YUMdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],AAPaft['Cumulative Return After Crash'].iloc[-1],AMZNaft['Cumulative Return After Crash'].iloc[-1],APTVaft['Cumulative Return After Crash'].iloc[-1],AZOaft['Cumulative Return After Crash'].iloc[-1],BBYaft['Cumulative Return After Crash'].iloc[-1],BKNGaft['Cumulative Return After Crash'].iloc[-1],BWAaft['Cumulative Return After Crash'].iloc[-1],KMXaft['Cumulative Return After Crash'].iloc[-1],CCLaft['Cumulative Return After Crash'].iloc[-1],CMGaft['Cumulative Return After Crash'].iloc[-1],DHIaft['Cumulative Return After Crash'].iloc[-1],DRIaft['Cumulative Return After Crash'].iloc[-1],DGaft['Cumulative Return After Crash'].iloc[-1],DLTRaft['Cumulative Return After Crash'].iloc[-1],DPZaft['Cumulative Return After Crash'].iloc[-1],EBAYaft['Cumulative Return After Crash'].iloc[-1],EXPEaft['Cumulative Return After Crash'].iloc[-1],Faft['Cumulative Return After Crash'].iloc[-1],GPSaft['Cumulative Return After Crash'].iloc[-1],GRMNaft['Cumulative Return After Crash'].iloc[-1],GMaft['Cumulative Return After Crash'].iloc[-1],GPCaft['Cumulative Return After Crash'].iloc[-1],HRBaft['Cumulative Return After Crash'].iloc[-1],HBIaft['Cumulative Return After Crash'].iloc[-1],HOGaft['Cumulative Return After Crash'].iloc[-1],HASaft['Cumulative Return After Crash'].iloc[-1],HLTaft['Cumulative Return After Crash'].iloc[-1],HDaft['Cumulative Return After Crash'].iloc[-1],KSSaft['Cumulative Return After Crash'].iloc[-1],LBaft['Cumulative Return After Crash'].iloc[-1],LVSaft['Cumulative Return After Crash'].iloc[-1],LEGaft['Cumulative Return After Crash'].iloc[-1],LENaft['Cumulative Return After Crash'].iloc[-1],LKQaft['Cumulative Return After Crash'].iloc[-1],LOWaft['Cumulative Return After Crash'].iloc[-1],MARaft['Cumulative Return After Crash'].iloc[-1],MCDaft['Cumulative Return After Crash'].iloc[-1],MGMaft['Cumulative Return After Crash'].iloc[-1],MHKaft['Cumulative Return After Crash'].iloc[-1],NWLaft['Cumulative Return After Crash'].iloc[-1],NKEaft['Cumulative Return After Crash'].iloc[-1],JWNaft['Cumulative Return After Crash'].iloc[-1],NCLHaft['Cumulative Return After Crash'].iloc[-1],NVRaft['Cumulative Return After Crash'].iloc[-1],ORLYaft['Cumulative Return After Crash'].iloc[-1],PHMaft['Cumulative Return After Crash'].iloc[-1],PVHaft['Cumulative Return After Crash'].iloc[-1],RLaft['Cumulative Return After Crash'].iloc[-1],ROSTaft['Cumulative Return After Crash'].iloc[-1],RCLaft['Cumulative Return After Crash'].iloc[-1],SBUXaft['Cumulative Return After Crash'].iloc[-1],TPRaft['Cumulative Return After Crash'].iloc[-1],TGTaft['Cumulative Return After Crash'].iloc[-1],TIFaft['Cumulative Return After Crash'].iloc[-1],TJXaft['Cumulative Return After Crash'].iloc[-1],TSCOaft['Cumulative Return After Crash'].iloc[-1],ULTAaft['Cumulative Return After Crash'].iloc[-1],UAAaft['Cumulative Return After Crash'].iloc[-1],UAaft['Cumulative Return After Crash'].iloc[-1],VFCaft['Cumulative Return After Crash'].iloc[-1],WHRaft['Cumulative Return After Crash'].iloc[-1],WYNNaft['Cumulative Return After Crash'].iloc[-1],YUMaft['Cumulative Return After Crash'].iloc[-1]]}                         

cddf2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','AAP','AMZN','APTV','AZO','BBY','BKNG','BWA','KMX','CCL','CMG','DHI','DRI','DG','DLTR','DPZ','EBAY','EXPE','F','GPS','GRMN','GM','GPC','HRB','HBI','HOG','HAS','HLT','HD','KSS','LB','LVS','LEG','LEN','LKQ','LOW','MAR','MCD','MGM','MHK','NWL','NKE','JWN','NCLH','NVR','ORLY','PHM','PVH','RL','ROST','RCL','SBUX','TPR','TGT','TIF','TJX','TSCO','ULTA','UAA','UA','VFC','WHR','WYNN','YUM'])

pd.DataFrame(cddf2)
Out[313]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
AAP 0.959102 0.935338 0.550955 1.861147
AMZN 1.410243 1.222873 0.883763 1.304899
APTV 0.929414 0.986143 0.510484 1.846237
AZO 0.992125 0.930728 0.668089 1.595545
... ... ... ... ...
UA 0.578483 0.878307 0.477912 1.378151
VFC 0.775897 0.953998 0.547565 1.485323
WHR 0.963461 1.049564 0.430775 2.130956
WYNN 0.855735 1.122874 0.431786 1.764982
YUM 0.972984 1.047077 0.544142 1.707714

64 rows × 4 columns

In [314]:
cddf2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[314]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
RCL 0.578185 0.914238 0.256740 2.463285
MGM 0.683664 1.017941 0.282931 2.373771
NCLH 0.418392 0.904868 0.199340 2.319545
LKQ 0.864896 0.971722 0.408583 2.178417
LEN 1.082951 1.207377 0.413206 2.170698
... ... ... ... ...
TGT 0.975988 0.949727 0.817605 1.256903
LVS 0.844951 1.075770 0.644276 1.219102
DLTR 1.040780 1.044365 0.819245 1.216446
DPZ 1.316119 1.304755 0.867912 1.162226
TIF 0.912279 1.003515 0.906699 1.002630

64 rows × 4 columns

In [315]:
#Table of Key Statistics of Consumer Discretionary Stocks
import pandas as pd
import statistics as stats

cdmeans = [AAPmean,AMZNmean,APTVmean,AZOmean,BBYmean,BKNGmean,BWAmean,KMXmean,CCLmean,CMGmean,DHImean,DRImean,DGmean,DLTRmean,DPZmean,EBAYmean,EXPEmean,Fmean,GPSmean,GRMNmean,GMmean,GPCmean,HRBmean,HBImean,HOGmean,HASmean,HLTmean,HDmean,KSSmean,LBmean,LVSmean,LEGmean,LENmean,LKQmean,LOWmean,MARmean,MCDmean,MGMmean,MHKmean,NWLmean,NKEmean,JWNmean,NCLHmean,NVRmean,ORLYmean,PHMmean,PVHmean,RLmean,ROSTmean,RCLmean,SBUXmean,TPRmean,TGTmean,TIFmean,TJXmean,TSCOmean,ULTAmean,UAAmean,UAmean,VFCmean,WHRmean,WYNNmean,YUMmean]
cdaveragemean = stats.mean(cdmeans)
cdvariances = [AAPvar,AMZNvar,APTVvar,AZOvar,BBYvar,BKNGvar,BWAvar,KMXvar,CCLvar,CMGvar,DHIvar,DRIvar,DGvar,DLTRvar,DPZvar,EBAYvar,EXPEvar,Fvar,GPSvar,GRMNvar,GMvar,GPCvar,HRBvar,HBIvar,HOGvar,HASvar,HLTvar,HDvar,KSSvar,LBvar,LVSvar,LEGvar,LENvar,LKQvar,LOWvar,MARvar,MCDvar,MGMvar,MHKvar,NWLvar,NKEvar,JWNvar,NCLHvar,NVRvar,ORLYvar,PHMvar,PVHvar,RLvar,ROSTvar,RCLvar,SBUXvar,TPRvar,TGTvar,TIFvar,TJXvar,TSCOvar,ULTAvar,UAAvar,UAvar,VFCvar,WHRvar,WYNNvar,YUMvar]
cdaveragevar = stats.mean(cdvariances)
cdkurtoses = [AAPkurt,AMZNkurt,APTVkurt,AZOkurt,BBYkurt,BKNGkurt,BWAkurt,KMXkurt,CCLkurt,CMGkurt,DHIkurt,DRIkurt,DGkurt,DLTRkurt,DPZkurt,EBAYkurt,EXPEkurt,Fkurt,GPSkurt,GRMNkurt,GMkurt,GPCkurt,HRBkurt,HBIkurt,HOGkurt,HASkurt,HLTkurt,HDkurt,KSSkurt,LBkurt,LVSkurt,LEGkurt,LENkurt,LKQkurt,LOWkurt,MARkurt,MCDkurt,MGMkurt,MHKkurt,NWLkurt,NKEkurt,JWNkurt,NCLHkurt,NVRkurt,ORLYkurt,PHMkurt,PVHkurt,RLkurt,ROSTkurt,RCLkurt,SBUXkurt,TPRkurt,TGTkurt,TIFkurt,TJXkurt,TSCOkurt,ULTAkurt,UAAkurt,UAkurt,VFCkurt,WHRkurt,WYNNkurt,YUMkurt]
cdaveragekurt = stats.mean(cdkurtoses)
cdskews = [AAPskew,AMZNskew,APTVskew,AZOskew,BBYskew,BKNGskew,BWAskew,KMXskew,CCLskew,CMGskew,DHIskew,DRIskew,DGskew,DLTRskew,DPZskew,EBAYskew,EXPEskew,Fskew,GPSskew,GRMNskew,GMskew,GPCskew,HRBskew,HBIskew,HOGskew,HASskew,HLTskew,HDskew,KSSskew,LBskew,LVSskew,LEGskew,LENskew,LKQskew,LOWskew,MARskew,MCDskew,MGMskew,MHKskew,NWLskew,NKEskew,JWNskew,NCLHskew,NVRskew,ORLYskew,PHMskew,PVHskew,RLskew,ROSTskew,RCLskew,SBUXskew,TPRskew,TGTskew,TIFskew,TJXskew,TSCOskew,ULTAskew,UAAskew,UAskew,VFCskew,WHRskew,WYNNskew,YUMskew]
cdaverageskew = stats.mean(cdskews)

Consumer_Discretionary = {'Mean': [[spmean],[cdaveragemean],[AAPmean], [AMZNmean],[APTVmean],[AZOmean],[BBYmean],[BKNGmean],[BWAmean],[KMXmean],[CCLmean],[CMGmean],[DHImean],[DRImean],[DGmean],[DLTRmean],[DPZmean],[EBAYmean],[EXPEmean],[Fmean],[GPSmean],[GRMNmean],[GMmean],[GPCmean],[HRBmean],[HBImean],[HOGmean],[HASmean],[HLTmean],[HDmean],[KSSmean],[LBmean],[LVSmean],[LEGmean],[LENmean],[LKQmean],[LOWmean],[MARmean],[MCDmean],[MGMmean],[MHKmean],[NWLmean],[NKEmean],[JWNmean],[NCLHmean],[NVRmean],[ORLYmean],[PHMmean],[PVHmean],[RLmean],[ROSTmean],[RCLmean],[SBUXmean],[TPRmean],[TGTmean],[TIFmean],[TJXmean],[TSCOmean],[ULTAmean],[UAAmean],[UAmean],[VFCmean],[WHRmean],[WYNNmean],[YUMmean]], 
        'Variance': [[spvar],[cdaveragevar],[AAPvar],[AMZNvar],[APTVvar],[AZOvar],[BBYvar],[BKNGvar],[BWAvar],[KMXvar],[CCLvar],[CMGvar],[DHIvar],[DRIvar],[DGvar],[DLTRvar],[DPZvar],[EBAYvar],[EXPEvar],[Fvar],[GPSvar],[GRMNvar],[GMvar],[GPCvar],[HRBvar],[HBIvar],[HOGvar],[HASvar],[HLTvar],[HDvar],[KSSvar],[LBvar],[LVSvar],[LEGvar],[LENvar],[LKQvar],[LOWvar],[MARvar],[MCDvar],[MGMvar],[MHKvar],[NWLvar],[NKEvar],[JWNvar],[NCLHvar],[NVRvar],[ORLYvar],[PHMvar],[PVHvar],[RLvar],[ROSTvar],[RCLvar],[SBUXvar],[TPRvar],[TGTvar],[TIFvar],[TJXvar],[TSCOvar],[ULTAvar],[UAAvar],[UAvar],[VFCvar],[WHRvar],[WYNNvar],[YUMvar]], 
          'Kurtosis': [[spkurt],[cdaveragekurt],[AAPkurt],[AMZNkurt],[APTVkurt],[AZOkurt],[BBYkurt],[BKNGkurt],[BWAkurt],[KMXkurt],[CCLkurt],[CMGkurt],[DHIkurt],[DRIkurt],[DGkurt],[DLTRkurt],[DPZkurt],[EBAYkurt],[EXPEkurt],[Fkurt],[GPSkurt],[GRMNkurt],[GMkurt],[GPCkurt],[HRBkurt],[HBIkurt],[HOGkurt],[HASkurt],[HLTkurt],[HDkurt],[KSSkurt],[LBkurt],[LVSkurt],[LEGkurt],[LENkurt],[LKQkurt],[LOWkurt],[MARkurt],[MCDkurt],[MGMkurt],[MHKkurt],[NWLkurt],[NKEkurt],[JWNkurt],[NCLHkurt],[NVRkurt],[ORLYkurt],[PHMkurt],[PVHkurt],[RLkurt],[ROSTkurt],[RCLkurt],[SBUXkurt],[TPRkurt],[TGTkurt],[TIFkurt],[TJXkurt],[TSCOkurt],[ULTAkurt],[UAAkurt],[UAkurt],[VFCkurt],[WHRkurt],[WYNNkurt],[YUMkurt]],
          'Skew': [[spskew],[cdaverageskew],[AAPskew],[AMZNskew],[APTVskew],[AZOskew],[BBYskew],[BKNGskew],[BWAskew],[KMXskew],[CCLskew],[CMGskew],[DHIskew],[DRIskew],[DGskew],[DLTRskew],[DPZskew],[EBAYskew],[EXPEskew],[Fskew],[GPSskew],[GRMNskew],[GMskew],[GPCskew],[HRBskew],[HBIskew],[HOGskew],[HASskew],[HLTskew],[HDskew],[KSSskew],[LBskew],[LVSskew],[LEGskew],[LENskew],[LKQskew],[LOWskew],[MARskew],[MCDskew],[MGMskew],[MHKskew],[NWLskew],[NKEskew],[JWNskew],[NCLHskew],[NVRskew],[ORLYskew],[PHMskew],[PVHskew],[RLskew],[ROSTskew],[RCLskew],[SBUXskew],[TPRskew],[TGTskew],[TIFskew],[TJXskew],[TSCOskew],[ULTAskew],[UAAskew],[UAskew],[VFCskew],[WHRskew],[WYNNskew],[YUMskew]],
        }

cddf = pd.DataFrame(Consumer_Discretionary, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','AAP','AMZN','APTV','AZO','BBY','BKNG','BWA','KMX','CCL','CMG','DHI','DRI','DG','DLTR','DPZ','EBAY','EXPE','F','GPS','GRMN','GM','GPC','HRB','HBI','HOG','HAS','HLT','HD','KSS','LB','LVS','LEG','LEN','LKQ','LOW','MAR','MCD','MGM','MHK','NWL','NKE','JWN','NCLH','NVR','ORLY','PHM','PVH','RL','ROST','RCL','SBUX','TPR','TGT','TIF','TJX','TSCO','ULTA','UAA','UA','VFC','WHR','WYNN','YUM']
)

pd.DataFrame(cddf)
Out[315]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [0.00023121301811639431] [0.002510920851468141] [5.574391146207624] [0.09733454086984512]
AAP [0.000405654633356166] [0.001446523117032696] [4.964585118466444] [-0.8821634742802407]
AMZN [0.0030383190256562127] [0.0006179359260908719] [1.941292927908207] [-0.06662344913402249]
APTV [0.0011716989166940237] [0.0033294615765948506] [8.69996509865124] [-1.0789253602489488]
... ... ... ... ...
UA [-0.003081519614322878] [0.0024539670927840847] [1.6144131507156834] [-0.6419928829494222]
VFC [-0.0010559345832449295] [0.0019170657918948703] [1.6313081897772568] [-0.008261744330507814]
WHR [0.0009489952053096169] [0.0025650425184336576] [10.272124097726966] [1.0537883380677897]
WYNN [0.0007633509469246925] [0.003920622506675341] [2.5420605665056986] [-0.43993904197864786]
YUM [0.0004305009248412207] [0.001352824088283344] [13.040998712931135] [1.779833414274901]

65 rows × 4 columns

In [316]:
cddf.sort_values("Mean", ascending=False)
Out[316]:
Mean Variance Kurtosis Skew
AMZN [0.0030383190256562127] [0.0006179359260908719] [1.941292927908207] [-0.06662344913402249]
EBAY [0.0029708167147754123] [0.0006438064186270174] [1.3400115106413137] [-0.0977265710414944]
CMG [0.002841989356347948] [0.001523138901289734] [3.8878645073037816] [0.4455518762423391]
DPZ [0.0027616815884926337] [0.0012423023685597073] [21.624310737329928] [3.0123269908472947]
LB [0.0026190312094838113] [0.005409307707411328] [7.114134919489996] [0.841480369612856]
... ... ... ... ...
NCLH [-0.001954845392150051] [0.009665917788103487] [4.22555414992546] [0.0645751837070995]
JWN [-0.0019980531681369827] [0.004122659841953598] [2.6252121925388376] [0.29743950855565204]
KSS [-0.002016167006775248] [0.00486418587549101] [3.243129731664541] [0.7025879644644234]
UAA [-0.0027369675079592357] [0.0026536436308267084] [1.7711929823939059] [-0.6276182153798722]
UA [-0.003081519614322878] [0.0024539670927840847] [1.6144131507156834] [-0.6419928829494222]

65 rows × 4 columns

Consumer Staples

In [317]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MO = web.DataReader('MO', 'yahoo', start, end)
MO['returns'] = MO['Close'].pct_change(1)
MO['Cumulative Return'] = (1 + MO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MOmean = MO['returns'].mean()
MOvar = MO['returns'].var()
MOkurt = MO['returns'].kurt()
MOskew = MO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MObef = web.DataReader('MO', 'yahoo', start, crashstart)
MObef['avgreturnbefore'] = MObef['Close'].pct_change(1)
MObef['Cumulative Return Before Crash'] = (1 + MObef['avgreturnbefore']).cumprod()

#period of crash
MOdur = web.DataReader('MO', 'yahoo', crashstart, crashend)
MOdur['avgreturnduring'] = MOdur['Close'].pct_change(1)
MOdur['Cumulative Return During Crash'] = (1 + MOdur['avgreturnduring']).cumprod()

#period after crash
MOaft = web.DataReader('MO', 'yahoo', crashend, end)
MOaft['avgreturnafter'] = MOaft['Close'].pct_change(1)
MOaft['Cumulative Return After Crash'] = (1 + MOaft['avgreturnafter']).cumprod()
In [318]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ADM = web.DataReader('ADM', 'yahoo', start, end)
ADM['returns'] = ADM['Close'].pct_change(1)
ADM['Cumulative Return'] = (1 + ADM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ADMmean = ADM['returns'].mean()
ADMvar = ADM['returns'].var()
ADMkurt = ADM['returns'].kurt()
ADMskew = ADM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ADMbef = web.DataReader('ADM', 'yahoo', start, crashstart)
ADMbef['avgreturnbefore'] = ADMbef['Close'].pct_change(1)
ADMbef['Cumulative Return Before Crash'] = (1 + ADMbef['avgreturnbefore']).cumprod()

#period of crash
ADMdur = web.DataReader('ADM', 'yahoo', crashstart, crashend)
ADMdur['avgreturnduring'] = ADMdur['Close'].pct_change(1)
ADMdur['Cumulative Return During Crash'] = (1 + ADMdur['avgreturnduring']).cumprod()

#period after crash
ADMaft = web.DataReader('ADM', 'yahoo', crashend, end)
ADMaft['avgreturnafter'] = ADMaft['Close'].pct_change(1)
ADMaft['Cumulative Return After Crash'] = (1 + ADMaft['avgreturnafter']).cumprod()
In [319]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BFB = web.DataReader('BF-B', 'yahoo', start, end)
BFB['returns'] = BFB['Close'].pct_change(1)
BFB['Cumulative Return'] = (1 + BFB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BFBmean = BFB['returns'].mean()
BFBvar = BFB['returns'].var()
BFBkurt = BFB['returns'].kurt()
BFBskew = BFB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BFBbef = web.DataReader('BF-B', 'yahoo', start, crashstart)
BFBbef['avgreturnbefore'] = BFBbef['Close'].pct_change(1)
BFBbef['Cumulative Return Before Crash'] = (1 + BFBbef['avgreturnbefore']).cumprod()

#period of crash
BFBdur = web.DataReader('BF-B', 'yahoo', crashstart, crashend)
BFBdur['avgreturnduring'] = BFBdur['Close'].pct_change(1)
BFBdur['Cumulative Return During Crash'] = (1 + BFBdur['avgreturnduring']).cumprod()

#period after crash
BFBaft = web.DataReader('BF-B', 'yahoo', crashend, end)
BFBaft['avgreturnafter'] = BFBaft['Close'].pct_change(1)
BFBaft['Cumulative Return After Crash'] = (1 + BFBaft['avgreturnafter']).cumprod()
In [320]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CPB = web.DataReader('CPB', 'yahoo', start, end)
CPB['returns'] = CPB['Close'].pct_change(1)
CPB['Cumulative Return'] = (1 + CPB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CPBmean = CPB['returns'].mean()
CPBvar = CPB['returns'].var()
CPBkurt = CPB['returns'].kurt()
CPBskew = CPB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CPBbef = web.DataReader('CPB', 'yahoo', start, crashstart)
CPBbef['avgreturnbefore'] = CPBbef['Close'].pct_change(1)
CPBbef['Cumulative Return Before Crash'] = (1 + CPBbef['avgreturnbefore']).cumprod()

#period of crash
CPBdur = web.DataReader('CPB', 'yahoo', crashstart, crashend)
CPBdur['avgreturnduring'] = CPBdur['Close'].pct_change(1)
CPBdur['Cumulative Return During Crash'] = (1 + CPBdur['avgreturnduring']).cumprod()

#period after crash
CPBaft = web.DataReader('CPB', 'yahoo', crashend, end)
CPBaft['avgreturnafter'] = CPBaft['Close'].pct_change(1)
CPBaft['Cumulative Return After Crash'] = (1 + CPBaft['avgreturnafter']).cumprod()
In [321]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CHD = web.DataReader('CHD', 'yahoo', start, end)
CHD['returns'] = CHD['Close'].pct_change(1)
CHD['Cumulative Return'] = (1 + CHD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CHDmean = CHD['returns'].mean()
CHDvar = CHD['returns'].var()
CHDkurt = CHD['returns'].kurt()
CHDskew = CHD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CHDbef = web.DataReader('CHD', 'yahoo', start, crashstart)
CHDbef['avgreturnbefore'] = CHDbef['Close'].pct_change(1)
CHDbef['Cumulative Return Before Crash'] = (1 + CHDbef['avgreturnbefore']).cumprod()

#period of crash
CHDdur = web.DataReader('CHD', 'yahoo', crashstart, crashend)
CHDdur['avgreturnduring'] = CHDdur['Close'].pct_change(1)
CHDdur['Cumulative Return During Crash'] = (1 + CHDdur['avgreturnduring']).cumprod()

#period after crash
CHDaft = web.DataReader('CHD', 'yahoo', crashend, end)
CHDaft['avgreturnafter'] = CHDaft['Close'].pct_change(1)
CHDaft['Cumulative Return After Crash'] = (1 + CHDaft['avgreturnafter']).cumprod()
In [322]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CLX = web.DataReader('CLX', 'yahoo', start, end)
CLX['returns'] = CLX['Close'].pct_change(1)
CLX['Cumulative Return'] = (1 + CLX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CLXmean = CLX['returns'].mean()
CLXvar = CLX['returns'].var()
CLXkurt = CLX['returns'].kurt()
CLXskew = CLX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CLXbef = web.DataReader('CLX', 'yahoo', start, crashstart)
CLXbef['avgreturnbefore'] = CLXbef['Close'].pct_change(1)
CLXbef['Cumulative Return Before Crash'] = (1 + CLXbef['avgreturnbefore']).cumprod()

#period of crash
CLXdur = web.DataReader('CLX', 'yahoo', crashstart, crashend)
CLXdur['avgreturnduring'] = CLXdur['Close'].pct_change(1)
CLXdur['Cumulative Return During Crash'] = (1 + CLXdur['avgreturnduring']).cumprod()

#period after crash
CLXaft = web.DataReader('CLX', 'yahoo', crashend, end)
CLXaft['avgreturnafter'] = CLXaft['Close'].pct_change(1)
CLXaft['Cumulative Return After Crash'] = (1 + CLXaft['avgreturnafter']).cumprod()
In [323]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KO = web.DataReader('KO', 'yahoo', start, end)
KO['returns'] = KO['Close'].pct_change(1)
KO['Cumulative Return'] = (1 + KO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KOmean = KO['returns'].mean()
KOvar = KO['returns'].var()
KOkurt = KO['returns'].kurt()
KOskew = KO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KObef = web.DataReader('KO', 'yahoo', start, crashstart)
KObef['avgreturnbefore'] = KObef['Close'].pct_change(1)
KObef['Cumulative Return Before Crash'] = (1 + KObef['avgreturnbefore']).cumprod()

#period of crash
KOdur = web.DataReader('KO', 'yahoo', crashstart, crashend)
KOdur['avgreturnduring'] = KOdur['Close'].pct_change(1)
KOdur['Cumulative Return During Crash'] = (1 + KOdur['avgreturnduring']).cumprod()

#period after crash
KOaft = web.DataReader('KO', 'yahoo', crashend, end)
KOaft['avgreturnafter'] = KOaft['Close'].pct_change(1)
KOaft['Cumulative Return After Crash'] = (1 + KOaft['avgreturnafter']).cumprod()
In [324]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CL = web.DataReader('CL', 'yahoo', start, end)
CL['returns'] = CL['Close'].pct_change(1)
CL['Cumulative Return'] = (1 + CL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CLmean = CL['returns'].mean()
CLvar = CL['returns'].var()
CLkurt = CL['returns'].kurt()
CLskew = CL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CLbef = web.DataReader('CL', 'yahoo', start, crashstart)
CLbef['avgreturnbefore'] = CLbef['Close'].pct_change(1)
CLbef['Cumulative Return Before Crash'] = (1 + CLbef['avgreturnbefore']).cumprod()

#period of crash
CLdur = web.DataReader('CL', 'yahoo', crashstart, crashend)
CLdur['avgreturnduring'] = CLdur['Close'].pct_change(1)
CLdur['Cumulative Return During Crash'] = (1 + CLdur['avgreturnduring']).cumprod()

#period after crash
CLaft = web.DataReader('CL', 'yahoo', crashend, end)
CLaft['avgreturnafter'] = CLaft['Close'].pct_change(1)
CLaft['Cumulative Return After Crash'] = (1 + CLaft['avgreturnafter']).cumprod()
In [325]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CAG = web.DataReader('CAG', 'yahoo', start, end)
CAG['returns'] = CAG['Close'].pct_change(1)
CAG['Cumulative Return'] = (1 + CAG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CAGmean = CAG['returns'].mean()
CAGvar = CAG['returns'].var()
CAGkurt = CAG['returns'].kurt()
CAGskew = CAG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CAGbef = web.DataReader('CAG', 'yahoo', start, crashstart)
CAGbef['avgreturnbefore'] = CAGbef['Close'].pct_change(1)
CAGbef['Cumulative Return Before Crash'] = (1 + CAGbef['avgreturnbefore']).cumprod()

#period of crash
CAGdur = web.DataReader('CAG', 'yahoo', crashstart, crashend)
CAGdur['avgreturnduring'] = CAGdur['Close'].pct_change(1)
CAGdur['Cumulative Return During Crash'] = (1 + CAGdur['avgreturnduring']).cumprod()

#period after crash
CAGaft = web.DataReader('CAG', 'yahoo', crashend, end)
CAGaft['avgreturnafter'] = CAGaft['Close'].pct_change(1)
CAGaft['Cumulative Return After Crash'] = (1 + CAGaft['avgreturnafter']).cumprod()
In [326]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
STZ = web.DataReader('STZ', 'yahoo', start, end)
STZ['returns'] = STZ['Close'].pct_change(1)
STZ['Cumulative Return'] = (1 + STZ['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
STZmean = STZ['returns'].mean()
STZvar = STZ['returns'].var()
STZkurt = STZ['returns'].kurt()
STZskew = STZ['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
STZbef = web.DataReader('STZ', 'yahoo', start, crashstart)
STZbef['avgreturnbefore'] = STZbef['Close'].pct_change(1)
STZbef['Cumulative Return Before Crash'] = (1 + STZbef['avgreturnbefore']).cumprod()

#period of crash
STZdur = web.DataReader('STZ', 'yahoo', crashstart, crashend)
STZdur['avgreturnduring'] = STZdur['Close'].pct_change(1)
STZdur['Cumulative Return During Crash'] = (1 + STZdur['avgreturnduring']).cumprod()

#period after crash
STZaft = web.DataReader('STZ', 'yahoo', crashend, end)
STZaft['avgreturnafter'] = STZaft['Close'].pct_change(1)
STZaft['Cumulative Return After Crash'] = (1 + STZaft['avgreturnafter']).cumprod()
In [327]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
COST = web.DataReader('COST', 'yahoo', start, end)
COST['returns'] = COST['Close'].pct_change(1)
COST['Cumulative Return'] = (1 + COST['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
COSTmean = COST['returns'].mean()
COSTvar = COST['returns'].var()
COSTkurt = COST['returns'].kurt()
COSTskew = COST['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
COSTbef = web.DataReader('COST', 'yahoo', start, crashstart)
COSTbef['avgreturnbefore'] = COSTbef['Close'].pct_change(1)
COSTbef['Cumulative Return Before Crash'] = (1 + COSTbef['avgreturnbefore']).cumprod()

#period of crash
COSTdur = web.DataReader('COST', 'yahoo', crashstart, crashend)
COSTdur['avgreturnduring'] = COSTdur['Close'].pct_change(1)
COSTdur['Cumulative Return During Crash'] = (1 + COSTdur['avgreturnduring']).cumprod()

#period after crash
COSTaft = web.DataReader('COST', 'yahoo', crashend, end)
COSTaft['avgreturnafter'] = COSTaft['Close'].pct_change(1)
COSTaft['Cumulative Return After Crash'] = (1 + COSTaft['avgreturnafter']).cumprod()
In [328]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
COTY = web.DataReader('COTY', 'yahoo', start, end)
COTY['returns'] = COTY['Close'].pct_change(1)
COTY['Cumulative Return'] = (1 + COTY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
COTYmean = COTY['returns'].mean()
COTYvar = COTY['returns'].var()
COTYkurt = COTY['returns'].kurt()
COTYskew = COTY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
COTYbef = web.DataReader('COTY', 'yahoo', start, crashstart)
COTYbef['avgreturnbefore'] = COTYbef['Close'].pct_change(1)
COTYbef['Cumulative Return Before Crash'] = (1 + COTYbef['avgreturnbefore']).cumprod()

#period of crash
COTYdur = web.DataReader('COTY', 'yahoo', crashstart, crashend)
COTYdur['avgreturnduring'] = COTYdur['Close'].pct_change(1)
COTYdur['Cumulative Return During Crash'] = (1 + COTYdur['avgreturnduring']).cumprod()

#period after crash
COTYaft = web.DataReader('COTY', 'yahoo', crashend, end)
COTYaft['avgreturnafter'] = COTYaft['Close'].pct_change(1)
COTYaft['Cumulative Return After Crash'] = (1 + COTYaft['avgreturnafter']).cumprod()
In [329]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EL = web.DataReader('EL', 'yahoo', start, end)
EL['returns'] = EL['Close'].pct_change(1)
EL['Cumulative Return'] = (1 + EL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ELmean = EL['returns'].mean()
ELvar = EL['returns'].var()
ELkurt = EL['returns'].kurt()
ELskew = EL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ELbef = web.DataReader('EL', 'yahoo', start, crashstart)
ELbef['avgreturnbefore'] = ELbef['Close'].pct_change(1)
ELbef['Cumulative Return Before Crash'] = (1 + ELbef['avgreturnbefore']).cumprod()

#period of crash
ELdur = web.DataReader('EL', 'yahoo', crashstart, crashend)
ELdur['avgreturnduring'] = ELdur['Close'].pct_change(1)
ELdur['Cumulative Return During Crash'] = (1 + ELdur['avgreturnduring']).cumprod()

#period after crash
ELaft = web.DataReader('EL', 'yahoo', crashend, end)
ELaft['avgreturnafter'] = ELaft['Close'].pct_change(1)
ELaft['Cumulative Return After Crash'] = (1 + ELaft['avgreturnafter']).cumprod()
In [330]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GIS = web.DataReader('GIS', 'yahoo', start, end)
GIS['returns'] = GIS['Close'].pct_change(1)
GIS['Cumulative Return'] = (1 + GIS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GISmean = GIS['returns'].mean()
GISvar = GIS['returns'].var()
GISkurt = GIS['returns'].kurt()
GISskew = GIS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GISbef = web.DataReader('GIS', 'yahoo', start, crashstart)
GISbef['avgreturnbefore'] = GISbef['Close'].pct_change(1)
GISbef['Cumulative Return Before Crash'] = (1 + GISbef['avgreturnbefore']).cumprod()

#period of crash
GISdur = web.DataReader('GIS', 'yahoo', crashstart, crashend)
GISdur['avgreturnduring'] = GISdur['Close'].pct_change(1)
GISdur['Cumulative Return During Crash'] = (1 + GISdur['avgreturnduring']).cumprod()

#period after crash
GISaft = web.DataReader('GIS', 'yahoo', crashend, end)
GISaft['avgreturnafter'] = GISaft['Close'].pct_change(1)
GISaft['Cumulative Return After Crash'] = (1 + GISaft['avgreturnafter']).cumprod()
In [331]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HSY = web.DataReader('HSY', 'yahoo', start, end)
HSY['returns'] = HSY['Close'].pct_change(1)
HSY['Cumulative Return'] = (1 + HSY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HSYmean = HSY['returns'].mean()
HSYvar = HSY['returns'].var()
HSYkurt = HSY['returns'].kurt()
HSYskew = HSY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HSYbef = web.DataReader('HSY', 'yahoo', start, crashstart)
HSYbef['avgreturnbefore'] = HSYbef['Close'].pct_change(1)
HSYbef['Cumulative Return Before Crash'] = (1 + HSYbef['avgreturnbefore']).cumprod()

#period of crash
HSYdur = web.DataReader('HSY', 'yahoo', crashstart, crashend)
HSYdur['avgreturnduring'] = HSYdur['Close'].pct_change(1)
HSYdur['Cumulative Return During Crash'] = (1 + HSYdur['avgreturnduring']).cumprod()

#period after crash
HSYaft = web.DataReader('HSY', 'yahoo', crashend, end)
HSYaft['avgreturnafter'] = HSYaft['Close'].pct_change(1)
HSYaft['Cumulative Return After Crash'] = (1 + HSYaft['avgreturnafter']).cumprod()
In [332]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HRL = web.DataReader('HRL', 'yahoo', start, end)
HRL['returns'] = HRL['Close'].pct_change(1)
HRL['Cumulative Return'] = (1 + HRL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HRLmean = HRL['returns'].mean()
HRLvar = HRL['returns'].var()
HRLkurt = HRL['returns'].kurt()
HRLskew = HRL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HRLbef = web.DataReader('HRL', 'yahoo', start, crashstart)
HRLbef['avgreturnbefore'] = HRLbef['Close'].pct_change(1)
HRLbef['Cumulative Return Before Crash'] = (1 + HRLbef['avgreturnbefore']).cumprod()

#period of crash
HRLdur = web.DataReader('HRL', 'yahoo', crashstart, crashend)
HRLdur['avgreturnduring'] = HRLdur['Close'].pct_change(1)
HRLdur['Cumulative Return During Crash'] = (1 + HRLdur['avgreturnduring']).cumprod()

#period after crash
HRLaft = web.DataReader('HRL', 'yahoo', crashend, end)
HRLaft['avgreturnafter'] = HRLaft['Close'].pct_change(1)
HRLaft['Cumulative Return After Crash'] = (1 + HRLaft['avgreturnafter']).cumprod()
In [333]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SJM = web.DataReader('SJM', 'yahoo', start, end)
SJM['returns'] = SJM['Close'].pct_change(1)
SJM['Cumulative Return'] = (1 + SJM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SJMmean = SJM['returns'].mean()
SJMvar = SJM['returns'].var()
SJMkurt = SJM['returns'].kurt()
SJMskew = SJM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SJMbef = web.DataReader('SJM', 'yahoo', start, crashstart)
SJMbef['avgreturnbefore'] = SJMbef['Close'].pct_change(1)
SJMbef['Cumulative Return Before Crash'] = (1 + SJMbef['avgreturnbefore']).cumprod()

#period of crash
SJMdur = web.DataReader('SJM', 'yahoo', crashstart, crashend)
SJMdur['avgreturnduring'] = SJMdur['Close'].pct_change(1)
SJMdur['Cumulative Return During Crash'] = (1 + SJMdur['avgreturnduring']).cumprod()

#period after crash
SJMaft = web.DataReader('SJM', 'yahoo', crashend, end)
SJMaft['avgreturnafter'] = SJMaft['Close'].pct_change(1)
SJMaft['Cumulative Return After Crash'] = (1 + SJMaft['avgreturnafter']).cumprod()
In [334]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
K = web.DataReader('K', 'yahoo', start, end)
K['returns'] = K['Close'].pct_change(1)
K['Cumulative Return'] = (1 + K['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
Kmean = K['returns'].mean()
Kvar = K['returns'].var()
Kkurt = K['returns'].kurt()
Kskew = K['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
Kbef = web.DataReader('K', 'yahoo', start, crashstart)
Kbef['avgreturnbefore'] = Kbef['Close'].pct_change(1)
Kbef['Cumulative Return Before Crash'] = (1 + Kbef['avgreturnbefore']).cumprod()

#period of crash
Kdur = web.DataReader('K', 'yahoo', crashstart, crashend)
Kdur['avgreturnduring'] = Kdur['Close'].pct_change(1)
Kdur['Cumulative Return During Crash'] = (1 + Kdur['avgreturnduring']).cumprod()

#period after crash
Kaft = web.DataReader('K', 'yahoo', crashend, end)
Kaft['avgreturnafter'] = Kaft['Close'].pct_change(1)
Kaft['Cumulative Return After Crash'] = (1 + Kaft['avgreturnafter']).cumprod()
In [335]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KMB = web.DataReader('KMB', 'yahoo', start, end)
KMB['returns'] = KMB['Close'].pct_change(1)
KMB['Cumulative Return'] = (1 + KMB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KMBmean = KMB['returns'].mean()
KMBvar = KMB['returns'].var()
KMBkurt = KMB['returns'].kurt()
KMBskew = KMB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KMBbef = web.DataReader('KMB', 'yahoo', start, crashstart)
KMBbef['avgreturnbefore'] = KMBbef['Close'].pct_change(1)
KMBbef['Cumulative Return Before Crash'] = (1 + KMBbef['avgreturnbefore']).cumprod()

#period of crash
KMBdur = web.DataReader('KMB', 'yahoo', crashstart, crashend)
KMBdur['avgreturnduring'] = KMBdur['Close'].pct_change(1)
KMBdur['Cumulative Return During Crash'] = (1 + KMBdur['avgreturnduring']).cumprod()

#period after crash
KMBaft = web.DataReader('KMB', 'yahoo', crashend, end)
KMBaft['avgreturnafter'] = KMBaft['Close'].pct_change(1)
KMBaft['Cumulative Return After Crash'] = (1 + KMBaft['avgreturnafter']).cumprod()
In [336]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KHC = web.DataReader('KHC', 'yahoo', start, end)
KHC['returns'] = KHC['Close'].pct_change(1)
KHC['Cumulative Return'] = (1 + KHC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KHCmean = KHC['returns'].mean()
KHCvar = KHC['returns'].var()
KHCkurt = KHC['returns'].kurt()
KHCskew = KHC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KHCbef = web.DataReader('KHC', 'yahoo', start, crashstart)
KHCbef['avgreturnbefore'] = KHCbef['Close'].pct_change(1)
KHCbef['Cumulative Return Before Crash'] = (1 + KHCbef['avgreturnbefore']).cumprod()

#period of crash
KHCdur = web.DataReader('KHC', 'yahoo', crashstart, crashend)
KHCdur['avgreturnduring'] = KHCdur['Close'].pct_change(1)
KHCdur['Cumulative Return During Crash'] = (1 + KHCdur['avgreturnduring']).cumprod()

#period after crash
KHCaft = web.DataReader('KHC', 'yahoo', crashend, end)
KHCaft['avgreturnafter'] = KHCaft['Close'].pct_change(1)
KHCaft['Cumulative Return After Crash'] = (1 + KHCaft['avgreturnafter']).cumprod()
In [337]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KR = web.DataReader('KR', 'yahoo', start, end)
KR['returns'] = KR['Close'].pct_change(1)
KR['Cumulative Return'] = (1 + KR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KRmean = KR['returns'].mean()
KRvar = KR['returns'].var()
KRkurt = KR['returns'].kurt()
KRskew = KR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KRbef = web.DataReader('KR', 'yahoo', start, crashstart)
KRbef['avgreturnbefore'] = KRbef['Close'].pct_change(1)
KRbef['Cumulative Return Before Crash'] = (1 + KRbef['avgreturnbefore']).cumprod()

#period of crash
KRdur = web.DataReader('KR', 'yahoo', crashstart, crashend)
KRdur['avgreturnduring'] = KRdur['Close'].pct_change(1)
KRdur['Cumulative Return During Crash'] = (1 + KRdur['avgreturnduring']).cumprod()

#period after crash
KRaft = web.DataReader('KR', 'yahoo', crashend, end)
KRaft['avgreturnafter'] = KRaft['Close'].pct_change(1)
KRaft['Cumulative Return After Crash'] = (1 + KRaft['avgreturnafter']).cumprod()
In [338]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LW = web.DataReader('LW', 'yahoo', start, end)
LW['returns'] = LW['Close'].pct_change(1)
LW['Cumulative Return'] = (1 + LW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LWmean = LW['returns'].mean()
LWvar = LW['returns'].var()
LWkurt = LW['returns'].kurt()
LWskew = LW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LWbef = web.DataReader('LW', 'yahoo', start, crashstart)
LWbef['avgreturnbefore'] = LWbef['Close'].pct_change(1)
LWbef['Cumulative Return Before Crash'] = (1 + LWbef['avgreturnbefore']).cumprod()

#period of crash
LWdur = web.DataReader('LW', 'yahoo', crashstart, crashend)
LWdur['avgreturnduring'] = LWdur['Close'].pct_change(1)
LWdur['Cumulative Return During Crash'] = (1 + LWdur['avgreturnduring']).cumprod()

#period after crash
LWaft = web.DataReader('LW', 'yahoo', crashend, end)
LWaft['avgreturnafter'] = LWaft['Close'].pct_change(1)
LWaft['Cumulative Return After Crash'] = (1 + LWaft['avgreturnafter']).cumprod()
In [339]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MKC = web.DataReader('MKC', 'yahoo', start, end)
MKC['returns'] = MKC['Close'].pct_change(1)
MKC['Cumulative Return'] = (1 + MKC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MKCmean = MKC['returns'].mean()
MKCvar = MKC['returns'].var()
MKCkurt = MKC['returns'].kurt()
MKCskew = MKC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MKCbef = web.DataReader('MKC', 'yahoo', start, crashstart)
MKCbef['avgreturnbefore'] = MKCbef['Close'].pct_change(1)
MKCbef['Cumulative Return Before Crash'] = (1 + MKCbef['avgreturnbefore']).cumprod()

#period of crash
MKCdur = web.DataReader('MKC', 'yahoo', crashstart, crashend)
MKCdur['avgreturnduring'] = MKCdur['Close'].pct_change(1)
MKCdur['Cumulative Return During Crash'] = (1 + MKCdur['avgreturnduring']).cumprod()

#period after crash
MKCaft = web.DataReader('MKC', 'yahoo', crashend, end)
MKCaft['avgreturnafter'] = MKCaft['Close'].pct_change(1)
MKCaft['Cumulative Return After Crash'] = (1 + MKCaft['avgreturnafter']).cumprod()
In [340]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TAP = web.DataReader('TAP', 'yahoo', start, end)
TAP['returns'] = TAP['Close'].pct_change(1)
TAP['Cumulative Return'] = (1 + TAP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TAPmean = TAP['returns'].mean()
TAPvar = TAP['returns'].var()
TAPkurt = TAP['returns'].kurt()
TAPskew = TAP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TAPbef = web.DataReader('TAP', 'yahoo', start, crashstart)
TAPbef['avgreturnbefore'] = TAPbef['Close'].pct_change(1)
TAPbef['Cumulative Return Before Crash'] = (1 + TAPbef['avgreturnbefore']).cumprod()

#period of crash
TAPdur = web.DataReader('TAP', 'yahoo', crashstart, crashend)
TAPdur['avgreturnduring'] = TAPdur['Close'].pct_change(1)
TAPdur['Cumulative Return During Crash'] = (1 + TAPdur['avgreturnduring']).cumprod()

#period after crash
TAPaft = web.DataReader('TAP', 'yahoo', crashend, end)
TAPaft['avgreturnafter'] = TAPaft['Close'].pct_change(1)
TAPaft['Cumulative Return After Crash'] = (1 + TAPaft['avgreturnafter']).cumprod()
In [341]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MDLZ = web.DataReader('MDLZ', 'yahoo', start, end)
MDLZ['returns'] = MDLZ['Close'].pct_change(1)
MDLZ['Cumulative Return'] = (1 + MDLZ['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MDLZmean = MDLZ['returns'].mean()
MDLZvar = MDLZ['returns'].var()
MDLZkurt = MDLZ['returns'].kurt()
MDLZskew = MDLZ['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MDLZbef = web.DataReader('MDLZ', 'yahoo', start, crashstart)
MDLZbef['avgreturnbefore'] = MDLZbef['Close'].pct_change(1)
MDLZbef['Cumulative Return Before Crash'] = (1 + MDLZbef['avgreturnbefore']).cumprod()

#period of crash
MDLZdur = web.DataReader('MDLZ', 'yahoo', crashstart, crashend)
MDLZdur['avgreturnduring'] = MDLZdur['Close'].pct_change(1)
MDLZdur['Cumulative Return During Crash'] = (1 + MDLZdur['avgreturnduring']).cumprod()

#period after crash
MDLZaft = web.DataReader('MDLZ', 'yahoo', crashend, end)
MDLZaft['avgreturnafter'] = MDLZaft['Close'].pct_change(1)
MDLZaft['Cumulative Return After Crash'] = (1 + MDLZaft['avgreturnafter']).cumprod()
In [342]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MNST = web.DataReader('MNST', 'yahoo', start, end)
MNST['returns'] = MNST['Close'].pct_change(1)
MNST['Cumulative Return'] = (1 + MNST['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MNSTmean = MNST['returns'].mean()
MNSTvar = MNST['returns'].var()
MNSTkurt = MNST['returns'].kurt()
MNSTskew = MNST['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MNSTbef = web.DataReader('MNST', 'yahoo', start, crashstart)
MNSTbef['avgreturnbefore'] = MNSTbef['Close'].pct_change(1)
MNSTbef['Cumulative Return Before Crash'] = (1 + MNSTbef['avgreturnbefore']).cumprod()

#period of crash
MNSTdur = web.DataReader('MNST', 'yahoo', crashstart, crashend)
MNSTdur['avgreturnduring'] = MNSTdur['Close'].pct_change(1)
MNSTdur['Cumulative Return During Crash'] = (1 + MNSTdur['avgreturnduring']).cumprod()

#period after crash
MNSTaft = web.DataReader('MNST', 'yahoo', crashend, end)
MNSTaft['avgreturnafter'] = MNSTaft['Close'].pct_change(1)
MNSTaft['Cumulative Return After Crash'] = (1 + MNSTaft['avgreturnafter']).cumprod()
In [343]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PEP = web.DataReader('PEP', 'yahoo', start, end)
PEP['returns'] = PEP['Close'].pct_change(1)
PEP['Cumulative Return'] = (1 + PEP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PEPmean = PEP['returns'].mean()
PEPvar = PEP['returns'].var()
PEPkurt = PEP['returns'].kurt()
PEPskew = PEP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PEPbef = web.DataReader('PEP', 'yahoo', start, crashstart)
PEPbef['avgreturnbefore'] = PEPbef['Close'].pct_change(1)
PEPbef['Cumulative Return Before Crash'] = (1 + PEPbef['avgreturnbefore']).cumprod()

#period of crash
PEPdur = web.DataReader('PEP', 'yahoo', crashstart, crashend)
PEPdur['avgreturnduring'] = PEPdur['Close'].pct_change(1)
PEPdur['Cumulative Return During Crash'] = (1 + PEPdur['avgreturnduring']).cumprod()

#period after crash
PEPaft = web.DataReader('PEP', 'yahoo', crashend, end)
PEPaft['avgreturnafter'] = PEPaft['Close'].pct_change(1)
PEPaft['Cumulative Return After Crash'] = (1 + PEPaft['avgreturnafter']).cumprod()
In [344]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PM = web.DataReader('PM', 'yahoo', start, end)
PM['returns'] = PM['Close'].pct_change(1)
PM['Cumulative Return'] = (1 + PM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PMmean = PM['returns'].mean()
PMvar = PM['returns'].var()
PMkurt = PM['returns'].kurt()
PMskew = PM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PMbef = web.DataReader('PM', 'yahoo', start, crashstart)
PMbef['avgreturnbefore'] = PMbef['Close'].pct_change(1)
PMbef['Cumulative Return Before Crash'] = (1 + PMbef['avgreturnbefore']).cumprod()

#period of crash
PMdur = web.DataReader('PM', 'yahoo', crashstart, crashend)
PMdur['avgreturnduring'] = PMdur['Close'].pct_change(1)
PMdur['Cumulative Return During Crash'] = (1 + PMdur['avgreturnduring']).cumprod()

#period after crash
PMaft = web.DataReader('PM', 'yahoo', crashend, end)
PMaft['avgreturnafter'] = PMaft['Close'].pct_change(1)
PMaft['Cumulative Return After Crash'] = (1 + PMaft['avgreturnafter']).cumprod()
In [345]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PG = web.DataReader('PG', 'yahoo', start, end)
PG['returns'] = PG['Close'].pct_change(1)
PG['Cumulative Return'] = (1 + PG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PGmean = PG['returns'].mean()
PGvar = PG['returns'].var()
PGkurt = PG['returns'].kurt()
PGskew = PG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PGbef = web.DataReader('PG', 'yahoo', start, crashstart)
PGbef['avgreturnbefore'] = PGbef['Close'].pct_change(1)
PGbef['Cumulative Return Before Crash'] = (1 + PGbef['avgreturnbefore']).cumprod()

#period of crash
PGdur = web.DataReader('PG', 'yahoo', crashstart, crashend)
PGdur['avgreturnduring'] = PGdur['Close'].pct_change(1)
PGdur['Cumulative Return During Crash'] = (1 + PGdur['avgreturnduring']).cumprod()

#period after crash
PGaft = web.DataReader('PG', 'yahoo', crashend, end)
PGaft['avgreturnafter'] = PGaft['Close'].pct_change(1)
PGaft['Cumulative Return After Crash'] = (1 + PGaft['avgreturnafter']).cumprod()
In [346]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SYY = web.DataReader('SYY', 'yahoo', start, end)
SYY['returns'] = SYY['Close'].pct_change(1)
SYY['Cumulative Return'] = (1 + SYY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SYYmean = SYY['returns'].mean()
SYYvar = SYY['returns'].var()
SYYkurt = SYY['returns'].kurt()
SYYskew = SYY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SYYbef = web.DataReader('SYY', 'yahoo', start, crashstart)
SYYbef['avgreturnbefore'] = SYYbef['Close'].pct_change(1)
SYYbef['Cumulative Return Before Crash'] = (1 + SYYbef['avgreturnbefore']).cumprod()

#period of crash
SYYdur = web.DataReader('SYY', 'yahoo', crashstart, crashend)
SYYdur['avgreturnduring'] = SYYdur['Close'].pct_change(1)
SYYdur['Cumulative Return During Crash'] = (1 + SYYdur['avgreturnduring']).cumprod()

#period after crash
SYYaft = web.DataReader('SYY', 'yahoo', crashend, end)
SYYaft['avgreturnafter'] = SYYaft['Close'].pct_change(1)
SYYaft['Cumulative Return After Crash'] = (1 + SYYaft['avgreturnafter']).cumprod()
In [347]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TSN = web.DataReader('TSN', 'yahoo', start, end)
TSN['returns'] = TSN['Close'].pct_change(1)
TSN['Cumulative Return'] = (1 + TSN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TSNmean = TSN['returns'].mean()
TSNvar = TSN['returns'].var()
TSNkurt = TSN['returns'].kurt()
TSNskew = TSN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TSNbef = web.DataReader('TSN', 'yahoo', start, crashstart)
TSNbef['avgreturnbefore'] = TSNbef['Close'].pct_change(1)
TSNbef['Cumulative Return Before Crash'] = (1 + TSNbef['avgreturnbefore']).cumprod()

#period of crash
TSNdur = web.DataReader('TSN', 'yahoo', crashstart, crashend)
TSNdur['avgreturnduring'] = TSNdur['Close'].pct_change(1)
TSNdur['Cumulative Return During Crash'] = (1 + TSNdur['avgreturnduring']).cumprod()

#period after crash
TSNaft = web.DataReader('TSN', 'yahoo', crashend, end)
TSNaft['avgreturnafter'] = TSNaft['Close'].pct_change(1)
TSNaft['Cumulative Return After Crash'] = (1 + TSNaft['avgreturnafter']).cumprod()
In [348]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WMT = web.DataReader('WMT', 'yahoo', start, end)
WMT['returns'] = WMT['Close'].pct_change(1)
WMT['Cumulative Return'] = (1 + WMT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WMTmean = WMT['returns'].mean()
WMTvar = WMT['returns'].var()
WMTkurt = WMT['returns'].kurt()
WMTskew = WMT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WMTbef = web.DataReader('WMT', 'yahoo', start, crashstart)
WMTbef['avgreturnbefore'] = WMTbef['Close'].pct_change(1)
WMTbef['Cumulative Return Before Crash'] = (1 + WMTbef['avgreturnbefore']).cumprod()

#period of crash
WMTdur = web.DataReader('WMT', 'yahoo', crashstart, crashend)
WMTdur['avgreturnduring'] = WMTdur['Close'].pct_change(1)
WMTdur['Cumulative Return During Crash'] = (1 + WMTdur['avgreturnduring']).cumprod()

#period after crash
WMTaft = web.DataReader('WMT', 'yahoo', crashend, end)
WMTaft['avgreturnafter'] = WMTaft['Close'].pct_change(1)
WMTaft['Cumulative Return After Crash'] = (1 + WMTaft['avgreturnafter']).cumprod()
In [349]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WBA = web.DataReader('WBA', 'yahoo', start, end)
WBA['returns'] = WBA['Close'].pct_change(1)
WBA['Cumulative Return'] = (1 + WBA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WBAmean = WBA['returns'].mean()
WBAvar = WBA['returns'].var()
WBAkurt = WBA['returns'].kurt()
WBAskew = WBA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WBAbef = web.DataReader('WBA', 'yahoo', start, crashstart)
WBAbef['avgreturnbefore'] = WBAbef['Close'].pct_change(1)
WBAbef['Cumulative Return Before Crash'] = (1 + WBAbef['avgreturnbefore']).cumprod()

#period of crash
WBAdur = web.DataReader('WBA', 'yahoo', crashstart, crashend)
WBAdur['avgreturnduring'] = WBAdur['Close'].pct_change(1)
WBAdur['Cumulative Return During Crash'] = (1 + WBAdur['avgreturnduring']).cumprod()

#period after crash
WBAaft = web.DataReader('WBA', 'yahoo', crashend, end)
WBAaft['avgreturnafter'] = WBAaft['Close'].pct_change(1)
WBAaft['Cumulative Return After Crash'] = (1 + WBAaft['avgreturnafter']).cumprod()
In [350]:
#KDE Plot for Consumer Staples Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
MO['returns'].plot(kind = 'kde', label ='MO', figsize = (10,8), alpha=0.5, title = 'KDE of Consumer Staples Stocks')
ADM['returns'].plot(kind = 'kde', label ='ADM', alpha=0.5)
BFB['returns'].plot(kind = 'kde', label ='BF.B', alpha=0.5)
CPB['returns'].plot(kind = 'kde', label ='CPB', alpha=0.5)
CHD['returns'].plot(kind = 'kde', label ='CHD', alpha=0.5)
CLX['returns'].plot(kind = 'kde', label ='CLX', alpha=0.5)
KO['returns'].plot(kind = 'kde', label ='KO', alpha=0.5)
CL['returns'].plot(kind = 'kde', label ='CL', alpha=0.5)
CAG['returns'].plot(kind = 'kde', label ='CAG', alpha=0.5)
STZ['returns'].plot(kind = 'kde', label ='STZ', alpha=0.5)
COST['returns'].plot(kind = 'kde', label ='COST', alpha=0.5)
COTY['returns'].plot(kind = 'kde', label ='COTY', alpha=0.5)
EL['returns'].plot(kind = 'kde', label ='EL', alpha=0.5)
GIS['returns'].plot(kind = 'kde', label ='GIS', alpha=0.5)
HSY['returns'].plot(kind = 'kde', label = 'HSY', alpha=0.5)
HRL['returns'].plot(kind = 'kde', label ='HRL', alpha=0.5)
SJM['returns'].plot(kind = 'kde', label ='SJM', alpha=0.5)
K['returns'].plot(kind = 'kde', label ='K', alpha=0.5)
KMB['returns'].plot(kind = 'kde', label ='KMB', alpha=0.5)
KHC['returns'].plot(kind = 'kde', label ='KHC', alpha=0.5)
KR['returns'].plot(kind = 'kde', label ='KR', alpha=0.5)
LW['returns'].plot(kind = 'kde', label ='LW', alpha=0.5)
MKC['returns'].plot(kind = 'kde', label ='MKC', alpha=0.5)
TAP['returns'].plot(kind = 'kde', label ='TAP', alpha=0.5)
MDLZ['returns'].plot(kind = 'kde', label ='MDLZ', alpha=0.5)
MNST['returns'].plot(kind = 'kde', label ='MNST', alpha=0.5)
PEP['returns'].plot(kind = 'kde', label ='PEP', alpha=0.5)
PM['returns'].plot(kind = 'kde', label ='PM', alpha=0.5)
PG['returns'].plot(kind = 'kde', label ='PG', alpha=0.5)
SYY['returns'].plot(kind = 'kde', label ='SYY', alpha=0.5)
TSN['returns'].plot(kind = 'kde', label ='TSN', alpha=0.5)
WMT['returns'].plot(kind = 'kde', label ='WMT', alpha=0.5)
WBA['returns'].plot(kind = 'kde', label ='WBA', alpha=0.5)

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [351]:
#Boxplots comparing returns for Consumer Staples Sector
box_df = pd.concat([sp['returns'],MO['returns'],ADM['returns'],BFB['returns'],CPB['returns'],CHD['returns'],CLX['returns'],KO['returns'],CL['returns'],CAG['returns'],STZ['returns'],COST['returns'],COTY['returns'],EL['returns'],GIS['returns'],HSY['returns'],HRL['returns'],SJM['returns'],K['returns'],KMB['returns'],KHC['returns'],KR['returns'],LW['returns'],MKC['returns'],TAP['returns'],MDLZ['returns'],MNST['returns'],PEP['returns'],PM['returns'],PG['returns'],SYY['returns'],TSN['returns'],WMT['returns'],WBA['returns']], axis=1)
box_df.columns = ['S&P 500','MO','ADM','BF.B','CPB','CHD','CLX','KO','CL','CAG','STZ','COST','COTY','EL','GIS','HSY','HRL','SJM','K','KMB','KHC','KR','LW','MKC','TAP','MDLZ','MNST','PEP','PM','PG','SYY','TSN','WMT','WBA']
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Consumer Staples Sector Returns')
Out[351]:
<matplotlib.axes._subplots.AxesSubplot at 0x13f9e60dcc0>
In [352]:
#Cumulative Returns of Consumer Staples Stocks for Whole period

sp['Cumulative Return'].plot(label="S&P 500")
MO['Cumulative Return'].plot(label='MO', figsize=(16,8), title = 'Cumulative Return of Consumer Staples Stocks')
ADM['Cumulative Return'].plot(label="ADM")
BFB['Cumulative Return'].plot(label="BF.B")
CPB['Cumulative Return'].plot(label="CPB")
CHD['Cumulative Return'].plot(label="CHD")
CLX['Cumulative Return'].plot(label="CLX")
KO['Cumulative Return'].plot(label="KO")
CL['Cumulative Return'].plot(label="CL")
CAG['Cumulative Return'].plot(label="CAG")
STZ['Cumulative Return'].plot(label="STZ")
COST['Cumulative Return'].plot(label="COST")
COTY['Cumulative Return'].plot(label="COTY")
EL['Cumulative Return'].plot(label="EL")
GIS['Cumulative Return'].plot(label="GIS")
HSY['Cumulative Return'].plot(label="HSY")
HRL['Cumulative Return'].plot(label="HRL")
SJM['Cumulative Return'].plot(label="SJM")
K['Cumulative Return'].plot(label="K")
KMB['Cumulative Return'].plot(label="KMB")
KHC['Cumulative Return'].plot(label="KHC")
KR['Cumulative Return'].plot(label="KR")
LW['Cumulative Return'].plot(label="LW")
MKC['Cumulative Return'].plot(label="MKC")
TAP['Cumulative Return'].plot(label="TAP")
MDLZ['Cumulative Return'].plot(label="MDLZ")
MNST['Cumulative Return'].plot(label="MNST")
PEP['Cumulative Return'].plot(label="PEP")
PM['Cumulative Return'].plot(label="PM")
PG['Cumulative Return'].plot(label="PG")
SYY['Cumulative Return'].plot(label="SYY")
TSN['Cumulative Return'].plot(label="TSN")
WMT['Cumulative Return'].plot(label="WMT")
WBA['Cumulative Return'].plot(label="WBA")

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [353]:
#Sorting by Cumulative Return

cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],MO['Cumulative Return'].iloc[-1],ADM['Cumulative Return'].iloc[-1],BFB['Cumulative Return'].iloc[-1],CPB['Cumulative Return'].iloc[-1],CHD['Cumulative Return'].iloc[-1],CLX['Cumulative Return'].iloc[-1],KO['Cumulative Return'].iloc[-1],CL['Cumulative Return'].iloc[-1],CAG['Cumulative Return'].iloc[-1],STZ['Cumulative Return'].iloc[-1],COST['Cumulative Return'].iloc[-1],COTY['Cumulative Return'].iloc[-1],EL['Cumulative Return'].iloc[-1],GIS['Cumulative Return'].iloc[-1],HSY['Cumulative Return'].iloc[-1],HRL['Cumulative Return'].iloc[-1],SJM['Cumulative Return'].iloc[-1],K['Cumulative Return'].iloc[-1],KMB['Cumulative Return'].iloc[-1],KHC['Cumulative Return'].iloc[-1],KR['Cumulative Return'].iloc[-1],LW['Cumulative Return'].iloc[-1],MKC['Cumulative Return'].iloc[-1],TAP['Cumulative Return'].iloc[-1],MDLZ['Cumulative Return'].iloc[-1],MNST['Cumulative Return'].iloc[-1],PEP['Cumulative Return'].iloc[-1],PM['Cumulative Return'].iloc[-1],PG['Cumulative Return'].iloc[-1],SYY['Cumulative Return'].iloc[-1],TSN['Cumulative Return'].iloc[-1],WMT['Cumulative Return'].iloc[-1],WBA['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],MObef['Cumulative Return Before Crash'].iloc[-1],ADMbef['Cumulative Return Before Crash'].iloc[-1],BFBbef['Cumulative Return Before Crash'].iloc[-1],CPBbef['Cumulative Return Before Crash'].iloc[-1],CHDbef['Cumulative Return Before Crash'].iloc[-1],CLXbef['Cumulative Return Before Crash'].iloc[-1],KObef['Cumulative Return Before Crash'].iloc[-1],CLbef['Cumulative Return Before Crash'].iloc[-1],CAGbef['Cumulative Return Before Crash'].iloc[-1],STZbef['Cumulative Return Before Crash'].iloc[-1],COSTbef['Cumulative Return Before Crash'].iloc[-1],COTYbef['Cumulative Return Before Crash'].iloc[-1],ELbef['Cumulative Return Before Crash'].iloc[-1],GISbef['Cumulative Return Before Crash'].iloc[-1],HSYbef['Cumulative Return Before Crash'].iloc[-1],HRLbef['Cumulative Return Before Crash'].iloc[-1],SJMbef['Cumulative Return Before Crash'].iloc[-1],Kbef['Cumulative Return Before Crash'].iloc[-1],KMBbef['Cumulative Return Before Crash'].iloc[-1],KHCbef['Cumulative Return Before Crash'].iloc[-1],KRbef['Cumulative Return Before Crash'].iloc[-1],LWbef['Cumulative Return Before Crash'].iloc[-1],MKCbef['Cumulative Return Before Crash'].iloc[-1],TAPbef['Cumulative Return Before Crash'].iloc[-1],MDLZbef['Cumulative Return Before Crash'].iloc[-1],MNSTbef['Cumulative Return Before Crash'].iloc[-1],PEPbef['Cumulative Return Before Crash'].iloc[-1],PMbef['Cumulative Return Before Crash'].iloc[-1],PGbef['Cumulative Return Before Crash'].iloc[-1],SYYbef['Cumulative Return Before Crash'].iloc[-1],TSNbef['Cumulative Return Before Crash'].iloc[-1],WMTbef['Cumulative Return Before Crash'].iloc[-1],WBAbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],MOdur['Cumulative Return During Crash'].iloc[-1],ADMdur['Cumulative Return During Crash'].iloc[-1],BFBdur['Cumulative Return During Crash'].iloc[-1],CPBdur['Cumulative Return During Crash'].iloc[-1],CHDdur['Cumulative Return During Crash'].iloc[-1],CLXdur['Cumulative Return During Crash'].iloc[-1],KOdur['Cumulative Return During Crash'].iloc[-1],CLdur['Cumulative Return During Crash'].iloc[-1],CAGdur['Cumulative Return During Crash'].iloc[-1],STZdur['Cumulative Return During Crash'].iloc[-1],COSTdur['Cumulative Return During Crash'].iloc[-1],COTYdur['Cumulative Return During Crash'].iloc[-1],ELdur['Cumulative Return During Crash'].iloc[-1],GISdur['Cumulative Return During Crash'].iloc[-1],HSYdur['Cumulative Return During Crash'].iloc[-1],HRLdur['Cumulative Return During Crash'].iloc[-1],SJMdur['Cumulative Return During Crash'].iloc[-1],Kdur['Cumulative Return During Crash'].iloc[-1],KMBdur['Cumulative Return During Crash'].iloc[-1],KHCdur['Cumulative Return During Crash'].iloc[-1],KRdur['Cumulative Return During Crash'].iloc[-1],LWdur['Cumulative Return During Crash'].iloc[-1],MKCdur['Cumulative Return During Crash'].iloc[-1],TAPdur['Cumulative Return During Crash'].iloc[-1],MDLZdur['Cumulative Return During Crash'].iloc[-1],MNSTdur['Cumulative Return During Crash'].iloc[-1],PEPdur['Cumulative Return During Crash'].iloc[-1],PMdur['Cumulative Return During Crash'].iloc[-1],PGdur['Cumulative Return During Crash'].iloc[-1],SYYdur['Cumulative Return During Crash'].iloc[-1],TSNdur['Cumulative Return During Crash'].iloc[-1],WMTdur['Cumulative Return During Crash'].iloc[-1],WBAdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],MOaft['Cumulative Return After Crash'].iloc[-1],ADMaft['Cumulative Return After Crash'].iloc[-1],BFBaft['Cumulative Return After Crash'].iloc[-1],CPBaft['Cumulative Return After Crash'].iloc[-1],CHDaft['Cumulative Return After Crash'].iloc[-1],CLXaft['Cumulative Return After Crash'].iloc[-1],KOaft['Cumulative Return After Crash'].iloc[-1],CLaft['Cumulative Return After Crash'].iloc[-1],CAGaft['Cumulative Return After Crash'].iloc[-1],STZaft['Cumulative Return After Crash'].iloc[-1],COSTaft['Cumulative Return After Crash'].iloc[-1],COTYaft['Cumulative Return After Crash'].iloc[-1],ELaft['Cumulative Return After Crash'].iloc[-1],GISaft['Cumulative Return After Crash'].iloc[-1],HSYaft['Cumulative Return After Crash'].iloc[-1],HRLaft['Cumulative Return After Crash'].iloc[-1],SJMaft['Cumulative Return After Crash'].iloc[-1],Kaft['Cumulative Return After Crash'].iloc[-1],KMBaft['Cumulative Return After Crash'].iloc[-1],KHCaft['Cumulative Return After Crash'].iloc[-1],KRaft['Cumulative Return After Crash'].iloc[-1],LWaft['Cumulative Return After Crash'].iloc[-1],MKCaft['Cumulative Return After Crash'].iloc[-1],TAPaft['Cumulative Return After Crash'].iloc[-1],MDLZaft['Cumulative Return After Crash'].iloc[-1],MNSTaft['Cumulative Return After Crash'].iloc[-1],PEPaft['Cumulative Return After Crash'].iloc[-1],PMaft['Cumulative Return After Crash'].iloc[-1],PGaft['Cumulative Return After Crash'].iloc[-1],SYYaft['Cumulative Return After Crash'].iloc[-1],TSNaft['Cumulative Return After Crash'].iloc[-1],WMTaft['Cumulative Return After Crash'].iloc[-1],WBAaft['Cumulative Return After Crash'].iloc[-1]]}                         

cosdf2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','MO','ADM','BF.B','CPB','CHD','CLX','KO','CL','CAG','STZ','COST','COTY','EL','GIS','HSY','HRL','SJM','K','KMB','KHC','KR','LW','MKC','TAP','MDLZ','MNST','PEP','PM','PG','SYY','TSN','WMT','WBA'])

pd.DataFrame(cosdf2)
Out[353]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
MO 0.836360 0.909200 0.688762 1.335564
ADM 0.990450 1.030282 0.662672 1.450699
BF.B 0.993809 1.054688 0.649616 1.450516
CPB 0.994017 0.984939 0.899246 1.122292
CHD 1.052039 1.087967 0.797667 1.212256
CLX 1.314504 1.092282 1.036365 1.161220
KO 0.904218 1.100018 0.628935 1.306975
CL 1.063455 1.103995 0.804550 1.197288
CAG 1.137121 1.016672 0.862115 1.297360
STZ 0.984427 1.130501 0.507056 1.717342
COST 1.052341 1.092945 0.881048 1.092845
COTY 0.438687 0.986183 0.450963 0.986408
EL 1.036893 1.067974 0.683229 1.421042
GIS 1.144465 1.015572 0.873453 1.290186
HSY 0.901971 1.066263 0.695959 1.215472
HRL 1.060773 1.006188 0.929277 1.134484
SJM 1.011010 1.040369 0.866407 1.121620
K 0.998326 0.999087 0.815593 1.225168
KMB 1.013019 1.034039 0.790181 1.239808
KHC 1.051241 0.883661 0.757476 1.570534
KR 1.175109 1.077424 1.043653 1.045045
LW 0.846904 1.128254 0.502634 1.493398
MKC 0.996849 0.956002 0.694806 1.500747
TAP 0.829259 1.083708 0.623714 1.226852
MDLZ 0.984814 1.106862 0.710196 1.252802
MNST 1.196059 1.165756 0.724006 1.417108
PEP 0.964603 1.057247 0.724580 1.259175
PM 0.924604 1.065043 0.685956 1.265589
PG 0.950213 1.016462 0.771844 1.211157
SYY 0.751189 0.935602 0.472820 1.698098
TSN 0.727476 0.883012 0.730964 1.127083
WMT 1.024181 0.991575 0.971026 1.063703
WBA 0.769818 0.880496 0.840756 1.039899
In [354]:
cosdf2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[354]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
STZ 0.984427 1.130501 0.507056 1.717342
SYY 0.751189 0.935602 0.472820 1.698098
KHC 1.051241 0.883661 0.757476 1.570534
MKC 0.996849 0.956002 0.694806 1.500747
LW 0.846904 1.128254 0.502634 1.493398
ADM 0.990450 1.030282 0.662672 1.450699
BF.B 0.993809 1.054688 0.649616 1.450516
S&P 500 1.026077 1.083678 0.663281 1.427519
EL 1.036893 1.067974 0.683229 1.421042
MNST 1.196059 1.165756 0.724006 1.417108
MO 0.836360 0.909200 0.688762 1.335564
KO 0.904218 1.100018 0.628935 1.306975
CAG 1.137121 1.016672 0.862115 1.297360
GIS 1.144465 1.015572 0.873453 1.290186
PM 0.924604 1.065043 0.685956 1.265589
PEP 0.964603 1.057247 0.724580 1.259175
MDLZ 0.984814 1.106862 0.710196 1.252802
KMB 1.013019 1.034039 0.790181 1.239808
TAP 0.829259 1.083708 0.623714 1.226852
K 0.998326 0.999087 0.815593 1.225168
HSY 0.901971 1.066263 0.695959 1.215472
CHD 1.052039 1.087967 0.797667 1.212256
PG 0.950213 1.016462 0.771844 1.211157
CL 1.063455 1.103995 0.804550 1.197288
CLX 1.314504 1.092282 1.036365 1.161220
HRL 1.060773 1.006188 0.929277 1.134484
TSN 0.727476 0.883012 0.730964 1.127083
CPB 0.994017 0.984939 0.899246 1.122292
SJM 1.011010 1.040369 0.866407 1.121620
COST 1.052341 1.092945 0.881048 1.092845
WMT 1.024181 0.991575 0.971026 1.063703
KR 1.175109 1.077424 1.043653 1.045045
WBA 0.769818 0.880496 0.840756 1.039899
COTY 0.438687 0.986183 0.450963 0.986408
In [355]:
#Table of Key Statistics of Consumer Staples Stocks
import pandas as pd
import statistics as stats

cosmeans = [MOmean,ADMmean,BFBmean,CPBmean,CHDmean,CLXmean,KOmean,CLmean,CAGmean,STZmean,COSTmean,COTYmean,ELmean,GISmean,HSYmean,HRLmean,SJMmean,Kmean,KMBmean,KHCmean,KRmean,LWmean,MKCmean,TAPmean,MDLZmean,MNSTmean,PEPmean,PMmean,PGmean,SYYmean,TSNmean,WMTmean,WBAmean]
cosaveragemean = stats.mean(cosmeans)
cosvariances = [MOvar,ADMvar,BFBvar,CPBvar,CHDvar,CLXvar,KOvar,CLvar,CAGvar,STZvar,COSTvar,COTYvar,ELvar,GISvar,HSYvar,HRLvar,SJMvar,Kvar,KMBvar,KHCvar,KRvar,LWvar,MKCvar,TAPvar,MDLZvar,MNSTvar,PEPvar,PMvar,PGvar,SYYvar,TSNvar,WMTvar,WBAvar]
cosaveragevar = stats.mean(cosvariances)
coskurtoses = [MOkurt,ADMkurt,BFBkurt,CPBkurt,CHDkurt,CLXkurt,KOkurt,CLkurt,CAGkurt,STZkurt,COSTkurt,COTYkurt,ELkurt,GISkurt,HSYkurt,HRLkurt,SJMkurt,Kkurt,KMBkurt,KHCkurt,KRkurt,LWkurt,MKCkurt,TAPkurt,MDLZkurt,MNSTkurt,PEPkurt,PMkurt,PGkurt,SYYkurt,TSNkurt,WMTkurt,WBAkurt]
cosaveragekurt = stats.mean(coskurtoses)
cosskews = [MOskew,ADMskew,BFBskew,CPBskew,CHDskew,CLXskew,KOskew,CLskew,CAGskew,STZskew,COSTskew,COTYskew,ELskew,GISskew,HSYskew,HRLskew,SJMskew,Kskew,KMBskew,KHCskew,KRskew,LWskew,MKCskew,TAPskew,MDLZskew,MNSTskew,PEPskew,PMskew,PGskew,SYYskew,TSNskew,WMTskew,WBAskew]
cosaverageskew = stats.mean(cosskews)

Consumer_Staples = {'Mean': [[spmean],[cosaveragemean],[MOmean], [ADMmean],[BFBmean],[CPBmean],[CHDmean],[CLXmean],[KOmean],[CLmean],[CAGmean],[STZmean],[COSTmean],[COTYmean],[ELmean],[GISmean],[HSYmean],[HRLmean],[SJMmean],[Kmean],[KMBmean],[KHCmean],[KRmean],[LWmean],[MKCmean],[TAPmean],[MDLZmean],[MNSTmean],[PEPmean],[PMmean],[PGmean],[SYYmean],[TSNmean],[WMTmean],[WBAmean]], 
        'Variance': [[spvar],[cosaveragevar],[MOvar],[ADMvar],[BFBvar],[CPBvar],[CHDvar],[CLXvar],[KOvar],[CLvar],[CAGvar],[STZvar],[COSTvar],[COTYvar],[ELvar],[GISvar],[HSYvar],[HRLvar],[SJMvar],[Kvar],[KMBvar],[KHCvar],[KRvar],[LWvar],[MKCvar],[TAPvar],[MDLZvar],[MNSTvar],[PEPvar],[PMvar],[PGvar],[SYYvar],[TSNvar],[WMTvar],[WBAvar]], 
          'Kurtosis': [[spkurt],[cosaveragekurt],[MOkurt],[ADMkurt],[BFBkurt],[CPBkurt],[CHDkurt],[CLXkurt],[KOkurt],[CLkurt],[CAGkurt],[STZkurt],[COSTkurt],[COTYkurt],[ELkurt],[GISkurt],[HSYkurt],[HRLkurt],[SJMkurt],[Kkurt],[KMBkurt],[KHCkurt],[KRkurt],[LWkurt],[MKCkurt],[TAPkurt],[MDLZkurt],[MNSTkurt],[PEPkurt],[PMkurt],[PGkurt],[SYYkurt],[TSNkurt],[WMTkurt],[WBAkurt]],
          'Skew': [[spskew],[cosaverageskew],[MOskew],[ADMskew],[BFBskew],[CPBskew],[CHDskew],[CLXskew],[KOskew],[CLskew],[CAGskew],[STZskew],[COSTskew],[COTYskew],[ELskew],[GISskew],[HSYskew],[HRLskew],[SJMskew],[Kskew],[KMBskew],[KHCskew],[KRskew],[LWskew],[MKCskew],[TAPskew],[MDLZskew],[MNSTskew],[PEPskew],[PMskew],[PGskew],[SYYskew],[TSNskew],[WMTskew],[WBAskew]],
        }

cosdf = pd.DataFrame(Consumer_Staples, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','MO','ADM','BF.B','CPB','CHD','CLX','KO','CL','CAG','STZ','COST','COTY','EL','GIS','HSY','HRL','SJM','K','KMB','KHC','KR','LW','MKC','TAP','MDLZ','MNST','PEP','PM','PG','SYY','TSN','WMT','WBA']
)

pd.DataFrame(cosdf)
Out[355]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [0.00019676499906751805] [0.0010751314445893423] [5.54886888026982] [0.2680625577374681]
MO [-0.0010369482476333155] [0.0007624359003413198] [2.5830224987912516] [-0.14118437986290194]
ADM [0.00041278445230106896] [0.0009862554618128533] [1.911936402616849] [0.10821699571209173]
BF.B [0.0004603863907997722] [0.0010222399966282373] [5.746953449167507] [-0.07431478006900819]
CPB [0.00025241957517087673] [0.0006098958418815329] [3.6628126351751344] [0.5983468022234435]
CHD [0.0007084472313821199] [0.000625768881728252] [7.8059535529276] [1.0478987970147913]
CLX [0.002436007615497283] [0.0005413013414724285] [9.172389587751045] [1.2923391598673268]
KO [-0.0004423033429159427] [0.0007111268178194148] [2.0120250935944624] [-0.48540112278010084]
CL [0.0008359241681982087] [0.0007081959193914247] [5.813019536933624] [0.7223737289133507]
CAG [0.0014551823974814995] [0.0008957672445228212] [7.605808808933102] [1.2246284439148167]
STZ [0.0005125512492373728] [0.0012811138085894718] [7.197551673438229] [0.11941836210245306]
COST [0.0006633735371313646] [0.000529500006700457] [4.920773797786121] [1.1491956318801033]
COTY [-0.003582726098061747] [0.005763136445382583] [3.653238867355072] [-0.0860081078453549]
EL [0.0008086468537380461] [0.0010377553894962783] [3.0409575366971824] [-0.48222295263139936]
GIS [0.001363409417096922] [0.0005881273519701768] [7.969922090108823] [0.06314336569145504]
HSY [-0.00033989833876066486] [0.0009369743399163552] [8.913018378554849] [-1.1842112465199521]
HRL [0.0007509567463039183] [0.00058119176454229] [9.014727902246939] [1.3884008263172454]
SJM [0.00040149341641800734] [0.0006372980601419525] [4.165168021407287] [0.3892809208233079]
K [0.0002975598848490381] [0.000622092017274955] [7.977464177913718] [-0.2753398348332712]
KMB [0.00041016842845568714] [0.0006224558533735062] [4.058553410676512] [0.34176733863674214]
KHC [0.0009990552547398104] [0.0012294306237743712] [10.118335514254678] [0.7952755709536857]
KR [0.0015694769483861968] [0.0005811734401047054] [1.9860671289065168] [0.037948605840349536]
LW [-0.0003789922789132359] [0.001850748671184842] [4.9059621570120555] [-0.5451036936536142]
MKC [0.000450600852441644] [0.000959701233364335] [7.696053514227711] [0.2441471027121674]
TAP [-0.0009342290052810294] [0.0011021810388369467] [1.951179205850603] [-0.1691688424752315]
MDLZ [0.00025910842082496214] [0.0007698932914150006] [5.47236269904318] [0.3417057939908901]
MNST [0.0018024902600567835] [0.0007707391800779107] [2.2492667971805362] [0.24465392127444155]
PEP [0.00018257730905770657] [0.0009409382905947655] [6.607004156291155] [0.002740098812404174]
PM [-0.00013780276616347033] [0.0009556421752238924] [4.722030079354874] [-0.8715821224845633]
PG [-3.916965917790213e-05] [0.0007425046029815125] [4.584515355073578] [0.48016418150478757]
SYY [-0.0008239713058782232] [0.002882755582266102] [4.860200324931962] [0.04365473878674848]
TSN [-0.001796467607800348] [0.0014899957069522678] [10.41510588227182] [1.1192360104757866]
WMT [0.0004741261048995218] [0.0005820846332021212] [7.6560170010337485] [1.0724651818509907]
WBA [-0.0015009928946538359] [0.0011589167584832125] [2.6632758113963453] [0.33359990919246624]
In [356]:
cosdf.sort_values("Mean", ascending=False)
Out[356]:
Mean Variance Kurtosis Skew
CLX [0.002436007615497283] [0.0005413013414724285] [9.172389587751045] [1.2923391598673268]
MNST [0.0018024902600567835] [0.0007707391800779107] [2.2492667971805362] [0.24465392127444155]
KR [0.0015694769483861968] [0.0005811734401047054] [1.9860671289065168] [0.037948605840349536]
CAG [0.0014551823974814995] [0.0008957672445228212] [7.605808808933102] [1.2246284439148167]
GIS [0.001363409417096922] [0.0005881273519701768] [7.969922090108823] [0.06314336569145504]
KHC [0.0009990552547398104] [0.0012294306237743712] [10.118335514254678] [0.7952755709536857]
CL [0.0008359241681982087] [0.0007081959193914247] [5.813019536933624] [0.7223737289133507]
EL [0.0008086468537380461] [0.0010377553894962783] [3.0409575366971824] [-0.48222295263139936]
HRL [0.0007509567463039183] [0.00058119176454229] [9.014727902246939] [1.3884008263172454]
CHD [0.0007084472313821199] [0.000625768881728252] [7.8059535529276] [1.0478987970147913]
COST [0.0006633735371313646] [0.000529500006700457] [4.920773797786121] [1.1491956318801033]
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
STZ [0.0005125512492373728] [0.0012811138085894718] [7.197551673438229] [0.11941836210245306]
WMT [0.0004741261048995218] [0.0005820846332021212] [7.6560170010337485] [1.0724651818509907]
BF.B [0.0004603863907997722] [0.0010222399966282373] [5.746953449167507] [-0.07431478006900819]
MKC [0.000450600852441644] [0.000959701233364335] [7.696053514227711] [0.2441471027121674]
ADM [0.00041278445230106896] [0.0009862554618128533] [1.911936402616849] [0.10821699571209173]
KMB [0.00041016842845568714] [0.0006224558533735062] [4.058553410676512] [0.34176733863674214]
SJM [0.00040149341641800734] [0.0006372980601419525] [4.165168021407287] [0.3892809208233079]
K [0.0002975598848490381] [0.000622092017274955] [7.977464177913718] [-0.2753398348332712]
MDLZ [0.00025910842082496214] [0.0007698932914150006] [5.47236269904318] [0.3417057939908901]
CPB [0.00025241957517087673] [0.0006098958418815329] [3.6628126351751344] [0.5983468022234435]
Average [0.00019676499906751805] [0.0010751314445893423] [5.54886888026982] [0.2680625577374681]
PEP [0.00018257730905770657] [0.0009409382905947655] [6.607004156291155] [0.002740098812404174]
PG [-3.916965917790213e-05] [0.0007425046029815125] [4.584515355073578] [0.48016418150478757]
PM [-0.00013780276616347033] [0.0009556421752238924] [4.722030079354874] [-0.8715821224845633]
HSY [-0.00033989833876066486] [0.0009369743399163552] [8.913018378554849] [-1.1842112465199521]
LW [-0.0003789922789132359] [0.001850748671184842] [4.9059621570120555] [-0.5451036936536142]
KO [-0.0004423033429159427] [0.0007111268178194148] [2.0120250935944624] [-0.48540112278010084]
SYY [-0.0008239713058782232] [0.002882755582266102] [4.860200324931962] [0.04365473878674848]
TAP [-0.0009342290052810294] [0.0011021810388369467] [1.951179205850603] [-0.1691688424752315]
MO [-0.0010369482476333155] [0.0007624359003413198] [2.5830224987912516] [-0.14118437986290194]
WBA [-0.0015009928946538359] [0.0011589167584832125] [2.6632758113963453] [0.33359990919246624]
TSN [-0.001796467607800348] [0.0014899957069522678] [10.41510588227182] [1.1192360104757866]
COTY [-0.003582726098061747] [0.005763136445382583] [3.653238867355072] [-0.0860081078453549]

Healthcare

In [357]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ABT = web.DataReader('ABT', 'yahoo', start, end)
ABT['returns'] = ABT['Close'].pct_change(1)
ABT['Cumulative Return'] = (1 + ABT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ABTmean = ABT['returns'].mean()
ABTvar = ABT['returns'].var()
ABTkurt = ABT['returns'].kurt()
ABTskew = ABT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ABTbef = web.DataReader('ABT', 'yahoo', start, crashstart)
ABTbef['avgreturnbefore'] = ABTbef['Close'].pct_change(1)
ABTbef['Cumulative Return Before Crash'] = (1 + ABTbef['avgreturnbefore']).cumprod()

#period of crash
ABTdur = web.DataReader('ABT', 'yahoo', crashstart, crashend)
ABTdur['avgreturnduring'] = ABTdur['Close'].pct_change(1)
ABTdur['Cumulative Return During Crash'] = (1 + ABTdur['avgreturnduring']).cumprod()

#period after crash
ABTaft = web.DataReader('ABT', 'yahoo', crashend, end)
ABTaft['avgreturnafter'] = ABTaft['Close'].pct_change(1)
ABTaft['Cumulative Return After Crash'] = (1 + ABTaft['avgreturnafter']).cumprod()
In [358]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ABBV = web.DataReader('ABBV', 'yahoo', start, end)
ABBV['returns'] = ABBV['Close'].pct_change(1)
ABBV['Cumulative Return'] = (1 + ABBV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ABBVmean = ABBV['returns'].mean()
ABBVvar = ABBV['returns'].var()
ABBVkurt = ABBV['returns'].kurt()
ABBVskew = ABBV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ABBVbef = web.DataReader('ABBV', 'yahoo', start, crashstart)
ABBVbef['avgreturnbefore'] = ABBVbef['Close'].pct_change(1)
ABBVbef['Cumulative Return Before Crash'] = (1 + ABBVbef['avgreturnbefore']).cumprod()

#period of crash
ABBVdur = web.DataReader('ABBV', 'yahoo', crashstart, crashend)
ABBVdur['avgreturnduring'] = ABBVdur['Close'].pct_change(1)
ABBVdur['Cumulative Return During Crash'] = (1 + ABBVdur['avgreturnduring']).cumprod()

#period after crash
ABBVaft = web.DataReader('ABBV', 'yahoo', crashend, end)
ABBVaft['avgreturnafter'] = ABBVaft['Close'].pct_change(1)
ABBVaft['Cumulative Return After Crash'] = (1 + ABBVaft['avgreturnafter']).cumprod()
In [359]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ABMD = web.DataReader('ABMD', 'yahoo', start, end)
ABMD['returns'] = ABMD['Close'].pct_change(1)
ABMD['Cumulative Return'] = (1 + ABMD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ABMDmean = ABMD['returns'].mean()
ABMDvar = ABMD['returns'].var()
ABMDkurt = ABMD['returns'].kurt()
ABMDskew = ABMD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ABMDbef = web.DataReader('ABMD', 'yahoo', start, crashstart)
ABMDbef['avgreturnbefore'] = ABMDbef['Close'].pct_change(1)
ABMDbef['Cumulative Return Before Crash'] = (1 + ABMDbef['avgreturnbefore']).cumprod()

#period of crash
ABMDdur = web.DataReader('ABMD', 'yahoo', crashstart, crashend)
ABMDdur['avgreturnduring'] = ABMDdur['Close'].pct_change(1)
ABMDdur['Cumulative Return During Crash'] = (1 + ABMDdur['avgreturnduring']).cumprod()

#period after crash
ABMDaft = web.DataReader('ABMD', 'yahoo', crashend, end)
ABMDaft['avgreturnafter'] = ABMDaft['Close'].pct_change(1)
ABMDaft['Cumulative Return After Crash'] = (1 + ABMDaft['avgreturnafter']).cumprod()
In [360]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
A = web.DataReader('A', 'yahoo', start, end)
A['returns'] = A['Close'].pct_change(1)
A['Cumulative Return'] = (1 + A['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
Amean = A['returns'].mean()
Avar = A['returns'].var()
Akurt = A['returns'].kurt()
Askew = A['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
Abef = web.DataReader('A', 'yahoo', start, crashstart)
Abef['avgreturnbefore'] = Abef['Close'].pct_change(1)
Abef['Cumulative Return Before Crash'] = (1 + Abef['avgreturnbefore']).cumprod()

#period of crash
Adur = web.DataReader('A', 'yahoo', crashstart, crashend)
Adur['avgreturnduring'] = Adur['Close'].pct_change(1)
Adur['Cumulative Return During Crash'] = (1 + Adur['avgreturnduring']).cumprod()

#period after crash
Aaft = web.DataReader('A', 'yahoo', crashend, end)
Aaft['avgreturnafter'] = Aaft['Close'].pct_change(1)
Aaft['Cumulative Return After Crash'] = (1 + Aaft['avgreturnafter']).cumprod()
In [361]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ALXN = web.DataReader('ALXN', 'yahoo', start, end)
ALXN['returns'] = ALXN['Close'].pct_change(1)
ALXN['Cumulative Return'] = (1 + ALXN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ALXNmean = ALXN['returns'].mean()
ALXNvar = ALXN['returns'].var()
ALXNkurt = ALXN['returns'].kurt()
ALXNskew = ALXN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ALXNbef = web.DataReader('ALXN', 'yahoo', start, crashstart)
ALXNbef['avgreturnbefore'] = ALXNbef['Close'].pct_change(1)
ALXNbef['Cumulative Return Before Crash'] = (1 + ALXNbef['avgreturnbefore']).cumprod()

#period of crash
ALXNdur = web.DataReader('ALXN', 'yahoo', crashstart, crashend)
ALXNdur['avgreturnduring'] = ALXNdur['Close'].pct_change(1)
ALXNdur['Cumulative Return During Crash'] = (1 + ALXNdur['avgreturnduring']).cumprod()

#period after crash
ALXNaft = web.DataReader('ALXN', 'yahoo', crashend, end)
ALXNaft['avgreturnafter'] = ALXNaft['Close'].pct_change(1)
ALXNaft['Cumulative Return After Crash'] = (1 + ALXNaft['avgreturnafter']).cumprod()
In [362]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ALGN = web.DataReader('ALGN', 'yahoo', start, end)
ALGN['returns'] = ALGN['Close'].pct_change(1)
ALGN['Cumulative Return'] = (1 + ALGN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ALGNmean = ALGN['returns'].mean()
ALGNvar = ALGN['returns'].var()
ALGNkurt = ALGN['returns'].kurt()
ALGNskew = ALGN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ALGNbef = web.DataReader('ALGN', 'yahoo', start, crashstart)
ALGNbef['avgreturnbefore'] = ALGNbef['Close'].pct_change(1)
ALGNbef['Cumulative Return Before Crash'] = (1 + ALGNbef['avgreturnbefore']).cumprod()

#period of crash
ALGNdur = web.DataReader('ALGN', 'yahoo', crashstart, crashend)
ALGNdur['avgreturnduring'] = ALGNdur['Close'].pct_change(1)
ALGNdur['Cumulative Return During Crash'] = (1 + ALGNdur['avgreturnduring']).cumprod()

#period after crash
ALGNaft = web.DataReader('ALGN', 'yahoo', crashend, end)
ALGNaft['avgreturnafter'] = ALGNaft['Close'].pct_change(1)
ALGNaft['Cumulative Return After Crash'] = (1 + ALGNaft['avgreturnafter']).cumprod()
In [363]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ABC = web.DataReader('ABC', 'yahoo', start, end)
ABC['returns'] = ABC['Close'].pct_change(1)
ABC['Cumulative Return'] = (1 + ABC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ABCmean = ABC['returns'].mean()
ABCvar = ABC['returns'].var()
ABCkurt = ABC['returns'].kurt()
ABCskew = ABC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ABCbef = web.DataReader('ABC', 'yahoo', start, crashstart)
ABCbef['avgreturnbefore'] = ABCbef['Close'].pct_change(1)
ABCbef['Cumulative Return Before Crash'] = (1 + ABCbef['avgreturnbefore']).cumprod()

#period of crash
ABCdur = web.DataReader('ABC', 'yahoo', crashstart, crashend)
ABCdur['avgreturnduring'] = ABCdur['Close'].pct_change(1)
ABCdur['Cumulative Return During Crash'] = (1 + ABCdur['avgreturnduring']).cumprod()

#period after crash
ABCaft = web.DataReader('ABC', 'yahoo', crashend, end)
ABCaft['avgreturnafter'] = ABCaft['Close'].pct_change(1)
ABCaft['Cumulative Return After Crash'] = (1 + ABCaft['avgreturnafter']).cumprod()
In [364]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AMGN = web.DataReader('AMGN', 'yahoo', start, end)
AMGN['returns'] = AMGN['Close'].pct_change(1)
AMGN['Cumulative Return'] = (1 + AMGN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AMGNmean = AMGN['returns'].mean()
AMGNvar = AMGN['returns'].var()
AMGNkurt = AMGN['returns'].kurt()
AMGNskew = AMGN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AMGNbef = web.DataReader('AMGN', 'yahoo', start, crashstart)
AMGNbef['avgreturnbefore'] = AMGNbef['Close'].pct_change(1)
AMGNbef['Cumulative Return Before Crash'] = (1 + AMGNbef['avgreturnbefore']).cumprod()

#period of crash
AMGNdur = web.DataReader('AMGN', 'yahoo', crashstart, crashend)
AMGNdur['avgreturnduring'] = AMGNdur['Close'].pct_change(1)
AMGNdur['Cumulative Return During Crash'] = (1 + AMGNdur['avgreturnduring']).cumprod()

#period after crash
AMGNaft = web.DataReader('AMGN', 'yahoo', crashend, end)
AMGNaft['avgreturnafter'] = AMGNaft['Close'].pct_change(1)
AMGNaft['Cumulative Return After Crash'] = (1 + AMGNaft['avgreturnafter']).cumprod()
In [365]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ANTM = web.DataReader('ANTM', 'yahoo', start, end)
ANTM['returns'] = ANTM['Close'].pct_change(1)
ANTM['Cumulative Return'] = (1 + ANTM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ANTMmean = ANTM['returns'].mean()
ANTMvar = ANTM['returns'].var()
ANTMkurt = ANTM['returns'].kurt()
ANTMskew = ANTM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ANTMbef = web.DataReader('ANTM', 'yahoo', start, crashstart)
ANTMbef['avgreturnbefore'] = ANTMbef['Close'].pct_change(1)
ANTMbef['Cumulative Return Before Crash'] = (1 + ANTMbef['avgreturnbefore']).cumprod()

#period of crash
ANTMdur = web.DataReader('ANTM', 'yahoo', crashstart, crashend)
ANTMdur['avgreturnduring'] = ANTMdur['Close'].pct_change(1)
ANTMdur['Cumulative Return During Crash'] = (1 + ANTMdur['avgreturnduring']).cumprod()

#period after crash
ANTMaft = web.DataReader('ANTM', 'yahoo', crashend, end)
ANTMaft['avgreturnafter'] = ANTMaft['Close'].pct_change(1)
ANTMaft['Cumulative Return After Crash'] = (1 + ANTMaft['avgreturnafter']).cumprod()
In [366]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BAX = web.DataReader('BAX', 'yahoo', start, end)
BAX['returns'] = BAX['Close'].pct_change(1)
BAX['Cumulative Return'] = (1 + BAX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BAXmean = BAX['returns'].mean()
BAXvar = BAX['returns'].var()
BAXkurt = BAX['returns'].kurt()
BAXskew = BAX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BAXbef = web.DataReader('BAX', 'yahoo', start, crashstart)
BAXbef['avgreturnbefore'] = BAXbef['Close'].pct_change(1)
BAXbef['Cumulative Return Before Crash'] = (1 + BAXbef['avgreturnbefore']).cumprod()

#period of crash
BAXdur = web.DataReader('BAX', 'yahoo', crashstart, crashend)
BAXdur['avgreturnduring'] = BAXdur['Close'].pct_change(1)
BAXdur['Cumulative Return During Crash'] = (1 + BAXdur['avgreturnduring']).cumprod()

#period after crash
BAXaft = web.DataReader('BAX', 'yahoo', crashend, end)
BAXaft['avgreturnafter'] = BAXaft['Close'].pct_change(1)
BAXaft['Cumulative Return After Crash'] = (1 + BAXaft['avgreturnafter']).cumprod()
In [367]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BDX = web.DataReader('BDX', 'yahoo', start, end)
BDX['returns'] = BDX['Close'].pct_change(1)
BDX['Cumulative Return'] = (1 + BDX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BDXmean = BDX['returns'].mean()
BDXvar = BDX['returns'].var()
BDXkurt = BDX['returns'].kurt()
BDXskew = BDX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BDXbef = web.DataReader('BDX', 'yahoo', start, crashstart)
BDXbef['avgreturnbefore'] = BDXbef['Close'].pct_change(1)
BDXbef['Cumulative Return Before Crash'] = (1 + BDXbef['avgreturnbefore']).cumprod()

#period of crash
BDXdur = web.DataReader('BDX', 'yahoo', crashstart, crashend)
BDXdur['avgreturnduring'] = BDXdur['Close'].pct_change(1)
BDXdur['Cumulative Return During Crash'] = (1 + BDXdur['avgreturnduring']).cumprod()

#period after crash
BDXaft = web.DataReader('BDX', 'yahoo', crashend, end)
BDXaft['avgreturnafter'] = BDXaft['Close'].pct_change(1)
BDXaft['Cumulative Return After Crash'] = (1 + BDXaft['avgreturnafter']).cumprod()
In [368]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BIIB = web.DataReader('BIIB', 'yahoo', start, end)
BIIB['returns'] = BIIB['Close'].pct_change(1)
BIIB['Cumulative Return'] = (1 + BIIB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BIIBmean = BIIB['returns'].mean()
BIIBvar = BIIB['returns'].var()
BIIBkurt = BIIB['returns'].kurt()
BIIBskew = BIIB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BIIBbef = web.DataReader('BIIB', 'yahoo', start, crashstart)
BIIBbef['avgreturnbefore'] = BIIBbef['Close'].pct_change(1)
BIIBbef['Cumulative Return Before Crash'] = (1 + BIIBbef['avgreturnbefore']).cumprod()

#period of crash
BIIBdur = web.DataReader('BIIB', 'yahoo', crashstart, crashend)
BIIBdur['avgreturnduring'] = BIIBdur['Close'].pct_change(1)
BIIBdur['Cumulative Return During Crash'] = (1 + BIIBdur['avgreturnduring']).cumprod()

#period after crash
BIIBaft = web.DataReader('BIIB', 'yahoo', crashend, end)
BIIBaft['avgreturnafter'] = BIIBaft['Close'].pct_change(1)
BIIBaft['Cumulative Return After Crash'] = (1 + BIIBaft['avgreturnafter']).cumprod()
In [369]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BSX = web.DataReader('BSX', 'yahoo', start, end)
BSX['returns'] = BSX['Close'].pct_change(1)
BSX['Cumulative Return'] = (1 + BSX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BSXmean = BSX['returns'].mean()
BSXvar = BSX['returns'].var()
BSXkurt = BSX['returns'].kurt()
BSXskew = BSX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BSXbef = web.DataReader('BSX', 'yahoo', start, crashstart)
BSXbef['avgreturnbefore'] = BSXbef['Close'].pct_change(1)
BSXbef['Cumulative Return Before Crash'] = (1 + BSXbef['avgreturnbefore']).cumprod()

#period of crash
BSXdur = web.DataReader('BSX', 'yahoo', crashstart, crashend)
BSXdur['avgreturnduring'] = BSXdur['Close'].pct_change(1)
BSXdur['Cumulative Return During Crash'] = (1 + BSXdur['avgreturnduring']).cumprod()

#period after crash
BSXaft = web.DataReader('BSX', 'yahoo', crashend, end)
BSXaft['avgreturnafter'] = BSXaft['Close'].pct_change(1)
BSXaft['Cumulative Return After Crash'] = (1 + BSXaft['avgreturnafter']).cumprod()
In [370]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BMY = web.DataReader('BMY', 'yahoo', start, end)
BMY['returns'] = BMY['Close'].pct_change(1)
BMY['Cumulative Return'] = (1 + BMY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BMYmean = BMY['returns'].mean()
BMYvar = BMY['returns'].var()
BMYkurt = BMY['returns'].kurt()
BMYskew = BMY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BMYbef = web.DataReader('BMY', 'yahoo', start, crashstart)
BMYbef['avgreturnbefore'] = BMYbef['Close'].pct_change(1)
BMYbef['Cumulative Return Before Crash'] = (1 + BMYbef['avgreturnbefore']).cumprod()

#period of crash
BMYdur = web.DataReader('BMY', 'yahoo', crashstart, crashend)
BMYdur['avgreturnduring'] = BMYdur['Close'].pct_change(1)
BMYdur['Cumulative Return During Crash'] = (1 + BMYdur['avgreturnduring']).cumprod()

#period after crash
BMYaft = web.DataReader('BMY', 'yahoo', crashend, end)
BMYaft['avgreturnafter'] = BMYaft['Close'].pct_change(1)
BMYaft['Cumulative Return After Crash'] = (1 + BMYaft['avgreturnafter']).cumprod()
In [371]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CAH = web.DataReader('CAH', 'yahoo', start, end)
CAH['returns'] = CAH['Close'].pct_change(1)
CAH['Cumulative Return'] = (1 + CAH['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CAHmean = CAH['returns'].mean()
CAHvar = CAH['returns'].var()
CAHkurt = CAH['returns'].kurt()
CAHskew = CAH['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CAHbef = web.DataReader('CAH', 'yahoo', start, crashstart)
CAHbef['avgreturnbefore'] = CAHbef['Close'].pct_change(1)
CAHbef['Cumulative Return Before Crash'] = (1 + CAHbef['avgreturnbefore']).cumprod()

#period of crash
CAHdur = web.DataReader('CAH', 'yahoo', crashstart, crashend)
CAHdur['avgreturnduring'] = CAHdur['Close'].pct_change(1)
CAHdur['Cumulative Return During Crash'] = (1 + CAHdur['avgreturnduring']).cumprod()

#period after crash
CAHaft = web.DataReader('CAH', 'yahoo', crashend, end)
CAHaft['avgreturnafter'] = CAHaft['Close'].pct_change(1)
CAHaft['Cumulative Return After Crash'] = (1 + CAHaft['avgreturnafter']).cumprod()
In [372]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CNC = web.DataReader('CNC', 'yahoo', start, end)
CNC['returns'] = CNC['Close'].pct_change(1)
CNC['Cumulative Return'] = (1 + CNC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CNCmean = CNC['returns'].mean()
CNCvar = CNC['returns'].var()
CNCkurt = CNC['returns'].kurt()
CNCskew = CNC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CNCbef = web.DataReader('CNC', 'yahoo', start, crashstart)
CNCbef['avgreturnbefore'] = CNCbef['Close'].pct_change(1)
CNCbef['Cumulative Return Before Crash'] = (1 + CNCbef['avgreturnbefore']).cumprod()

#period of crash
CNCdur = web.DataReader('CNC', 'yahoo', crashstart, crashend)
CNCdur['avgreturnduring'] = CNCdur['Close'].pct_change(1)
CNCdur['Cumulative Return During Crash'] = (1 + CNCdur['avgreturnduring']).cumprod()

#period after crash
CNCaft = web.DataReader('CNC', 'yahoo', crashend, end)
CNCaft['avgreturnafter'] = CNCaft['Close'].pct_change(1)
CNCaft['Cumulative Return After Crash'] = (1 + CNCaft['avgreturnafter']).cumprod()
In [373]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CERN = web.DataReader('CERN', 'yahoo', start, end)
CERN['returns'] = CERN['Close'].pct_change(1)
CERN['Cumulative Return'] = (1 + CERN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CERNmean = CERN['returns'].mean()
CERNvar = CERN['returns'].var()
CERNkurt = CERN['returns'].kurt()
CERNskew = CERN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CERNbef = web.DataReader('CERN', 'yahoo', start, crashstart)
CERNbef['avgreturnbefore'] = CERNbef['Close'].pct_change(1)
CERNbef['Cumulative Return Before Crash'] = (1 + CERNbef['avgreturnbefore']).cumprod()

#period of crash
CERNdur = web.DataReader('CERN', 'yahoo', crashstart, crashend)
CERNdur['avgreturnduring'] = CERNdur['Close'].pct_change(1)
CERNdur['Cumulative Return During Crash'] = (1 + CERNdur['avgreturnduring']).cumprod()

#period after crash
CERNaft = web.DataReader('CERN', 'yahoo', crashend, end)
CERNaft['avgreturnafter'] = CERNaft['Close'].pct_change(1)
CERNaft['Cumulative Return After Crash'] = (1 + CERNaft['avgreturnafter']).cumprod()
In [374]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CI = web.DataReader('CI', 'yahoo', start, end)
CI['returns'] = CI['Close'].pct_change(1)
CI['Cumulative Return'] = (1 + CI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CImean = CI['returns'].mean()
CIvar = CI['returns'].var()
CIkurt = CI['returns'].kurt()
CIskew = CI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CIbef = web.DataReader('CI', 'yahoo', start, crashstart)
CIbef['avgreturnbefore'] = CIbef['Close'].pct_change(1)
CIbef['Cumulative Return Before Crash'] = (1 + CIbef['avgreturnbefore']).cumprod()

#period of crash
CIdur = web.DataReader('CI', 'yahoo', crashstart, crashend)
CIdur['avgreturnduring'] = CIdur['Close'].pct_change(1)
CIdur['Cumulative Return During Crash'] = (1 + CIdur['avgreturnduring']).cumprod()

#period after crash
CIaft = web.DataReader('CI', 'yahoo', crashend, end)
CIaft['avgreturnafter'] = CIaft['Close'].pct_change(1)
CIaft['Cumulative Return After Crash'] = (1 + CIaft['avgreturnafter']).cumprod()
In [375]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
COO = web.DataReader('COO', 'yahoo', start, end)
COO['returns'] = COO['Close'].pct_change(1)
COO['Cumulative Return'] = (1 + COO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
COOmean = COO['returns'].mean()
COOvar = COO['returns'].var()
COOkurt = COO['returns'].kurt()
COOskew = COO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
COObef = web.DataReader('COO', 'yahoo', start, crashstart)
COObef['avgreturnbefore'] = COObef['Close'].pct_change(1)
COObef['Cumulative Return Before Crash'] = (1 + COObef['avgreturnbefore']).cumprod()

#period of crash
COOdur = web.DataReader('COO', 'yahoo', crashstart, crashend)
COOdur['avgreturnduring'] = COOdur['Close'].pct_change(1)
COOdur['Cumulative Return During Crash'] = (1 + COOdur['avgreturnduring']).cumprod()

#period after crash
COOaft = web.DataReader('COO', 'yahoo', crashend, end)
COOaft['avgreturnafter'] = COOaft['Close'].pct_change(1)
COOaft['Cumulative Return After Crash'] = (1 + COOaft['avgreturnafter']).cumprod()
In [376]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CVS = web.DataReader('CVS', 'yahoo', start, end)
CVS['returns'] = CVS['Close'].pct_change(1)
CVS['Cumulative Return'] = (1 + CVS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CVSmean = CVS['returns'].mean()
CVSvar = CVS['returns'].var()
CVSkurt = CVS['returns'].kurt()
CVSskew = CVS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CVSbef = web.DataReader('CVS', 'yahoo', start, crashstart)
CVSbef['avgreturnbefore'] = CVSbef['Close'].pct_change(1)
CVSbef['Cumulative Return Before Crash'] = (1 + CVSbef['avgreturnbefore']).cumprod()

#period of crash
CVSdur = web.DataReader('CVS', 'yahoo', crashstart, crashend)
CVSdur['avgreturnduring'] = CVSdur['Close'].pct_change(1)
CVSdur['Cumulative Return During Crash'] = (1 + CVSdur['avgreturnduring']).cumprod()

#period after crash
CVSaft = web.DataReader('CVS', 'yahoo', crashend, end)
CVSaft['avgreturnafter'] = CVSaft['Close'].pct_change(1)
CVSaft['Cumulative Return After Crash'] = (1 + CVSaft['avgreturnafter']).cumprod()
In [377]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DHR = web.DataReader('DHR', 'yahoo', start, end)
DHR['returns'] = DHR['Close'].pct_change(1)
DHR['Cumulative Return'] = (1 + DHR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DHRmean = DHR['returns'].mean()
DHRvar = DHR['returns'].var()
DHRkurt = DHR['returns'].kurt()
DHRskew = DHR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DHRbef = web.DataReader('DHR', 'yahoo', start, crashstart)
DHRbef['avgreturnbefore'] = DHRbef['Close'].pct_change(1)
DHRbef['Cumulative Return Before Crash'] = (1 + DHRbef['avgreturnbefore']).cumprod()

#period of crash
DHRdur = web.DataReader('DHR', 'yahoo', crashstart, crashend)
DHRdur['avgreturnduring'] = DHRdur['Close'].pct_change(1)
DHRdur['Cumulative Return During Crash'] = (1 + DHRdur['avgreturnduring']).cumprod()

#period after crash
DHRaft = web.DataReader('DHR', 'yahoo', crashend, end)
DHRaft['avgreturnafter'] = DHRaft['Close'].pct_change(1)
DHRaft['Cumulative Return After Crash'] = (1 + DHRaft['avgreturnafter']).cumprod()
In [378]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DVA = web.DataReader('DVA', 'yahoo', start, end)
DVA['returns'] = DVA['Close'].pct_change(1)
DVA['Cumulative Return'] = (1 + DVA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DVAmean = DVA['returns'].mean()
DVAvar = DVA['returns'].var()
DVAkurt = DVA['returns'].kurt()
DVAskew = DVA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DVAbef = web.DataReader('DVA', 'yahoo', start, crashstart)
DVAbef['avgreturnbefore'] = DVAbef['Close'].pct_change(1)
DVAbef['Cumulative Return Before Crash'] = (1 + DVAbef['avgreturnbefore']).cumprod()

#period of crash
DVAdur = web.DataReader('DVA', 'yahoo', crashstart, crashend)
DVAdur['avgreturnduring'] = DVAdur['Close'].pct_change(1)
DVAdur['Cumulative Return During Crash'] = (1 + DVAdur['avgreturnduring']).cumprod()

#period after crash
DVAaft = web.DataReader('DVA', 'yahoo', crashend, end)
DVAaft['avgreturnafter'] = DVAaft['Close'].pct_change(1)
DVAaft['Cumulative Return After Crash'] = (1 + DVAaft['avgreturnafter']).cumprod()
In [379]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
XRAY = web.DataReader('XRAY', 'yahoo', start, end)
XRAY['returns'] = XRAY['Close'].pct_change(1)
XRAY['Cumulative Return'] = (1 + XRAY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
XRAYmean = XRAY['returns'].mean()
XRAYvar = XRAY['returns'].var()
XRAYkurt = XRAY['returns'].kurt()
XRAYskew = XRAY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
XRAYbef = web.DataReader('XRAY', 'yahoo', start, crashstart)
XRAYbef['avgreturnbefore'] = XRAYbef['Close'].pct_change(1)
XRAYbef['Cumulative Return Before Crash'] = (1 + XRAYbef['avgreturnbefore']).cumprod()

#period of crash
XRAYdur = web.DataReader('XRAY', 'yahoo', crashstart, crashend)
XRAYdur['avgreturnduring'] = XRAYdur['Close'].pct_change(1)
XRAYdur['Cumulative Return During Crash'] = (1 + XRAYdur['avgreturnduring']).cumprod()

#period after crash
XRAYaft = web.DataReader('XRAY', 'yahoo', crashend, end)
XRAYaft['avgreturnafter'] = XRAYaft['Close'].pct_change(1)
XRAYaft['Cumulative Return After Crash'] = (1 + XRAYaft['avgreturnafter']).cumprod()
In [380]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DXCM = web.DataReader('DXCM', 'yahoo', start, end)
DXCM['returns'] = DXCM['Close'].pct_change(1)
DXCM['Cumulative Return'] = (1 + DXCM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DXCMmean = DXCM['returns'].mean()
DXCMvar = DXCM['returns'].var()
DXCMkurt = DXCM['returns'].kurt()
DXCMskew = DXCM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DXCMbef = web.DataReader('DXCM', 'yahoo', start, crashstart)
DXCMbef['avgreturnbefore'] = DXCMbef['Close'].pct_change(1)
DXCMbef['Cumulative Return Before Crash'] = (1 + DXCMbef['avgreturnbefore']).cumprod()

#period of crash
DXCMdur = web.DataReader('DXCM', 'yahoo', crashstart, crashend)
DXCMdur['avgreturnduring'] = DXCMdur['Close'].pct_change(1)
DXCMdur['Cumulative Return During Crash'] = (1 + DXCMdur['avgreturnduring']).cumprod()

#period after crash
DXCMaft = web.DataReader('DXCM', 'yahoo', crashend, end)
DXCMaft['avgreturnafter'] = DXCMaft['Close'].pct_change(1)
DXCMaft['Cumulative Return After Crash'] = (1 + DXCMaft['avgreturnafter']).cumprod()
In [381]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EW = web.DataReader('EW', 'yahoo', start, end)
EW['returns'] = EW['Close'].pct_change(1)
EW['Cumulative Return'] = (1 + EW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EWmean = EW['returns'].mean()
EWvar = EW['returns'].var()
EWkurt = EW['returns'].kurt()
EWskew = EW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EWbef = web.DataReader('EW', 'yahoo', start, crashstart)
EWbef['avgreturnbefore'] = EWbef['Close'].pct_change(1)
EWbef['Cumulative Return Before Crash'] = (1 + EWbef['avgreturnbefore']).cumprod()

#period of crash
EWdur = web.DataReader('EW', 'yahoo', crashstart, crashend)
EWdur['avgreturnduring'] = EWdur['Close'].pct_change(1)
EWdur['Cumulative Return During Crash'] = (1 + EWdur['avgreturnduring']).cumprod()

#period after crash
EWaft = web.DataReader('EW', 'yahoo', crashend, end)
EWaft['avgreturnafter'] = EWaft['Close'].pct_change(1)
EWaft['Cumulative Return After Crash'] = (1 + EWaft['avgreturnafter']).cumprod()
In [382]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GILD = web.DataReader('GILD', 'yahoo', start, end)
GILD['returns'] = GILD['Close'].pct_change(1)
GILD['Cumulative Return'] = (1 + GILD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GILDmean = GILD['returns'].mean()
GILDvar = GILD['returns'].var()
GILDkurt = GILD['returns'].kurt()
GILDskew = GILD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GILDbef = web.DataReader('GILD', 'yahoo', start, crashstart)
GILDbef['avgreturnbefore'] = GILDbef['Close'].pct_change(1)
GILDbef['Cumulative Return Before Crash'] = (1 + GILDbef['avgreturnbefore']).cumprod()

#period of crash
GILDdur = web.DataReader('GILD', 'yahoo', crashstart, crashend)
GILDdur['avgreturnduring'] = GILDdur['Close'].pct_change(1)
GILDdur['Cumulative Return During Crash'] = (1 + GILDdur['avgreturnduring']).cumprod()

#period after crash
GILDaft = web.DataReader('GILD', 'yahoo', crashend, end)
GILDaft['avgreturnafter'] = GILDaft['Close'].pct_change(1)
GILDaft['Cumulative Return After Crash'] = (1 + GILDaft['avgreturnafter']).cumprod()
In [383]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HCA = web.DataReader('HCA', 'yahoo', start, end)
HCA['returns'] = HCA['Close'].pct_change(1)
HCA['Cumulative Return'] = (1 + HCA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HCAmean = HCA['returns'].mean()
HCAvar = HCA['returns'].var()
HCAkurt = HCA['returns'].kurt()
HCAskew = HCA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HCAbef = web.DataReader('HCA', 'yahoo', start, crashstart)
HCAbef['avgreturnbefore'] = HCAbef['Close'].pct_change(1)
HCAbef['Cumulative Return Before Crash'] = (1 + HCAbef['avgreturnbefore']).cumprod()

#period of crash
HCAdur = web.DataReader('HCA', 'yahoo', crashstart, crashend)
HCAdur['avgreturnduring'] = HCAdur['Close'].pct_change(1)
HCAdur['Cumulative Return During Crash'] = (1 + HCAdur['avgreturnduring']).cumprod()

#period after crash
HCAaft = web.DataReader('HCA', 'yahoo', crashend, end)
HCAaft['avgreturnafter'] = HCAaft['Close'].pct_change(1)
HCAaft['Cumulative Return After Crash'] = (1 + HCAaft['avgreturnafter']).cumprod()
In [384]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HSIC = web.DataReader('HSIC', 'yahoo', start, end)
HSIC['returns'] = HSIC['Close'].pct_change(1)
HSIC['Cumulative Return'] = (1 + HSIC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HSICmean = HSIC['returns'].mean()
HSICvar = HSIC['returns'].var()
HSICkurt = HSIC['returns'].kurt()
HSICskew = HSIC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HSICbef = web.DataReader('HSIC', 'yahoo', start, crashstart)
HSICbef['avgreturnbefore'] = HSICbef['Close'].pct_change(1)
HSICbef['Cumulative Return Before Crash'] = (1 + HSICbef['avgreturnbefore']).cumprod()

#period of crash
HSICdur = web.DataReader('HSIC', 'yahoo', crashstart, crashend)
HSICdur['avgreturnduring'] = HSICdur['Close'].pct_change(1)
HSICdur['Cumulative Return During Crash'] = (1 + HSICdur['avgreturnduring']).cumprod()

#period after crash
HSICaft = web.DataReader('HSIC', 'yahoo', crashend, end)
HSICaft['avgreturnafter'] = HSICaft['Close'].pct_change(1)
HSICaft['Cumulative Return After Crash'] = (1 + HSICaft['avgreturnafter']).cumprod()
In [385]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HOLX = web.DataReader('HOLX', 'yahoo', start, end)
HOLX['returns'] = HOLX['Close'].pct_change(1)
HOLX['Cumulative Return'] = (1 + HOLX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HOLXmean = HOLX['returns'].mean()
HOLXvar = HOLX['returns'].var()
HOLXkurt = HOLX['returns'].kurt()
HOLXskew = HOLX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HOLXbef = web.DataReader('HOLX', 'yahoo', start, crashstart)
HOLXbef['avgreturnbefore'] = HOLXbef['Close'].pct_change(1)
HOLXbef['Cumulative Return Before Crash'] = (1 + HOLXbef['avgreturnbefore']).cumprod()

#period of crash
HOLXdur = web.DataReader('HOLX', 'yahoo', crashstart, crashend)
HOLXdur['avgreturnduring'] = HOLXdur['Close'].pct_change(1)
HOLXdur['Cumulative Return During Crash'] = (1 + HOLXdur['avgreturnduring']).cumprod()

#period after crash
HOLXaft = web.DataReader('HOLX', 'yahoo', crashend, end)
HOLXaft['avgreturnafter'] = HOLXaft['Close'].pct_change(1)
HOLXaft['Cumulative Return After Crash'] = (1 + HOLXaft['avgreturnafter']).cumprod()
In [386]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HUM = web.DataReader('HUM', 'yahoo', start, end)
HUM['returns'] = HUM['Close'].pct_change(1)
HUM['Cumulative Return'] = (1 + HUM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HUMmean = HUM['returns'].mean()
HUMvar = HUM['returns'].var()
HUMkurt = HUM['returns'].kurt()
HUMskew = HUM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HUMbef = web.DataReader('HUM', 'yahoo', start, crashstart)
HUMbef['avgreturnbefore'] = HUMbef['Close'].pct_change(1)
HUMbef['Cumulative Return Before Crash'] = (1 + HUMbef['avgreturnbefore']).cumprod()

#period of crash
HUMdur = web.DataReader('HUM', 'yahoo', crashstart, crashend)
HUMdur['avgreturnduring'] = HUMdur['Close'].pct_change(1)
HUMdur['Cumulative Return During Crash'] = (1 + HUMdur['avgreturnduring']).cumprod()

#period after crash
HUMaft = web.DataReader('HUM', 'yahoo', crashend, end)
HUMaft['avgreturnafter'] = HUMaft['Close'].pct_change(1)
HUMaft['Cumulative Return After Crash'] = (1 + HUMaft['avgreturnafter']).cumprod()
In [387]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IDXX = web.DataReader('IDXX', 'yahoo', start, end)
IDXX['returns'] = IDXX['Close'].pct_change(1)
IDXX['Cumulative Return'] = (1 + IDXX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IDXXmean = IDXX['returns'].mean()
IDXXvar = IDXX['returns'].var()
IDXXkurt = IDXX['returns'].kurt()
IDXXskew = IDXX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IDXXbef = web.DataReader('IDXX', 'yahoo', start, crashstart)
IDXXbef['avgreturnbefore'] = IDXXbef['Close'].pct_change(1)
IDXXbef['Cumulative Return Before Crash'] = (1 + IDXXbef['avgreturnbefore']).cumprod()

#period of crash
IDXXdur = web.DataReader('IDXX', 'yahoo', crashstart, crashend)
IDXXdur['avgreturnduring'] = IDXXdur['Close'].pct_change(1)
IDXXdur['Cumulative Return During Crash'] = (1 + IDXXdur['avgreturnduring']).cumprod()

#period after crash
IDXXaft = web.DataReader('IDXX', 'yahoo', crashend, end)
IDXXaft['avgreturnafter'] = IDXXaft['Close'].pct_change(1)
IDXXaft['Cumulative Return After Crash'] = (1 + IDXXaft['avgreturnafter']).cumprod()
In [388]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ILMN = web.DataReader('ILMN', 'yahoo', start, end)
ILMN['returns'] = ILMN['Close'].pct_change(1)
ILMN['Cumulative Return'] = (1 + ILMN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ILMNmean = ILMN['returns'].mean()
ILMNvar = ILMN['returns'].var()
ILMNkurt = ILMN['returns'].kurt()
ILMNskew = ILMN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ILMNbef = web.DataReader('ILMN', 'yahoo', start, crashstart)
ILMNbef['avgreturnbefore'] = ILMNbef['Close'].pct_change(1)
ILMNbef['Cumulative Return Before Crash'] = (1 + ILMNbef['avgreturnbefore']).cumprod()

#period of crash
ILMNdur = web.DataReader('ILMN', 'yahoo', crashstart, crashend)
ILMNdur['avgreturnduring'] = ILMNdur['Close'].pct_change(1)
ILMNdur['Cumulative Return During Crash'] = (1 + ILMNdur['avgreturnduring']).cumprod()

#period after crash
ILMNaft = web.DataReader('ILMN', 'yahoo', crashend, end)
ILMNaft['avgreturnafter'] = ILMNaft['Close'].pct_change(1)
ILMNaft['Cumulative Return After Crash'] = (1 + ILMNaft['avgreturnafter']).cumprod()
In [389]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
INCY = web.DataReader('INCY', 'yahoo', start, end)
INCY['returns'] = INCY['Close'].pct_change(1)
INCY['Cumulative Return'] = (1 + INCY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
INCYmean = INCY['returns'].mean()
INCYvar = INCY['returns'].var()
INCYkurt = INCY['returns'].kurt()
INCYskew = INCY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
INCYbef = web.DataReader('INCY', 'yahoo', start, crashstart)
INCYbef['avgreturnbefore'] = INCYbef['Close'].pct_change(1)
INCYbef['Cumulative Return Before Crash'] = (1 + INCYbef['avgreturnbefore']).cumprod()

#period of crash
INCYdur = web.DataReader('INCY', 'yahoo', crashstart, crashend)
INCYdur['avgreturnduring'] = INCYdur['Close'].pct_change(1)
INCYdur['Cumulative Return During Crash'] = (1 + INCYdur['avgreturnduring']).cumprod()

#period after crash
INCYaft = web.DataReader('INCY', 'yahoo', crashend, end)
INCYaft['avgreturnafter'] = INCYaft['Close'].pct_change(1)
INCYaft['Cumulative Return After Crash'] = (1 + INCYaft['avgreturnafter']).cumprod()
In [390]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ISRG = web.DataReader('ISRG', 'yahoo', start, end)
ISRG['returns'] = ISRG['Close'].pct_change(1)
ISRG['Cumulative Return'] = (1 + ISRG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ISRGmean = ISRG['returns'].mean()
ISRGvar = ISRG['returns'].var()
ISRGkurt = ISRG['returns'].kurt()
ISRGskew = ISRG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ISRGbef = web.DataReader('ISRG', 'yahoo', start, crashstart)
ISRGbef['avgreturnbefore'] = ISRGbef['Close'].pct_change(1)
ISRGbef['Cumulative Return Before Crash'] = (1 + ISRGbef['avgreturnbefore']).cumprod()

#period of crash
ISRGdur = web.DataReader('ISRG', 'yahoo', crashstart, crashend)
ISRGdur['avgreturnduring'] = ISRGdur['Close'].pct_change(1)
ISRGdur['Cumulative Return During Crash'] = (1 + ISRGdur['avgreturnduring']).cumprod()

#period after crash
ISRGaft = web.DataReader('ISRG', 'yahoo', crashend, end)
ISRGaft['avgreturnafter'] = ISRGaft['Close'].pct_change(1)
ISRGaft['Cumulative Return After Crash'] = (1 + ISRGaft['avgreturnafter']).cumprod()
In [391]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IQV = web.DataReader('IQV', 'yahoo', start, end)
IQV['returns'] = IQV['Close'].pct_change(1)
IQV['Cumulative Return'] = (1 + IQV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IQVmean = IQV['returns'].mean()
IQVvar = IQV['returns'].var()
IQVkurt = IQV['returns'].kurt()
IQVskew = IQV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IQVbef = web.DataReader('IQV', 'yahoo', start, crashstart)
IQVbef['avgreturnbefore'] = IQVbef['Close'].pct_change(1)
IQVbef['Cumulative Return Before Crash'] = (1 + IQVbef['avgreturnbefore']).cumprod()

#period of crash
IQVdur = web.DataReader('IQV', 'yahoo', crashstart, crashend)
IQVdur['avgreturnduring'] = IQVdur['Close'].pct_change(1)
IQVdur['Cumulative Return During Crash'] = (1 + IQVdur['avgreturnduring']).cumprod()

#period after crash
IQVaft = web.DataReader('IQV', 'yahoo', crashend, end)
IQVaft['avgreturnafter'] = IQVaft['Close'].pct_change(1)
IQVaft['Cumulative Return After Crash'] = (1 + IQVaft['avgreturnafter']).cumprod()
In [392]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
JNJ = web.DataReader('JNJ', 'yahoo', start, end)
JNJ['returns'] = JNJ['Close'].pct_change(1)
JNJ['Cumulative Return'] = (1 + JNJ['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
JNJmean = JNJ['returns'].mean()
JNJvar = JNJ['returns'].var()
JNJkurt = JNJ['returns'].kurt()
JNJskew = JNJ['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
JNJbef = web.DataReader('JNJ', 'yahoo', start, crashstart)
JNJbef['avgreturnbefore'] = JNJbef['Close'].pct_change(1)
JNJbef['Cumulative Return Before Crash'] = (1 + JNJbef['avgreturnbefore']).cumprod()

#period of crash
JNJdur = web.DataReader('JNJ', 'yahoo', crashstart, crashend)
JNJdur['avgreturnduring'] = JNJdur['Close'].pct_change(1)
JNJdur['Cumulative Return During Crash'] = (1 + JNJdur['avgreturnduring']).cumprod()

#period after crash
JNJaft = web.DataReader('JNJ', 'yahoo', crashend, end)
JNJaft['avgreturnafter'] = JNJaft['Close'].pct_change(1)
JNJaft['Cumulative Return After Crash'] = (1 + JNJaft['avgreturnafter']).cumprod()
In [393]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LH = web.DataReader('LH', 'yahoo', start, end)
LH['returns'] = LH['Close'].pct_change(1)
LH['Cumulative Return'] = (1 + LH['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LHmean = LH['returns'].mean()
LHvar = LH['returns'].var()
LHkurt = LH['returns'].kurt()
LHskew = LH['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LHbef = web.DataReader('LH', 'yahoo', start, crashstart)
LHbef['avgreturnbefore'] = LHbef['Close'].pct_change(1)
LHbef['Cumulative Return Before Crash'] = (1 + LHbef['avgreturnbefore']).cumprod()

#period of crash
LHdur = web.DataReader('LH', 'yahoo', crashstart, crashend)
LHdur['avgreturnduring'] = LHdur['Close'].pct_change(1)
LHdur['Cumulative Return During Crash'] = (1 + LHdur['avgreturnduring']).cumprod()

#period after crash
LHaft = web.DataReader('LH', 'yahoo', crashend, end)
LHaft['avgreturnafter'] = LHaft['Close'].pct_change(1)
LHaft['Cumulative Return After Crash'] = (1 + LHaft['avgreturnafter']).cumprod()
In [394]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LLY = web.DataReader('LLY', 'yahoo', start, end)
LLY['returns'] = LLY['Close'].pct_change(1)
LLY['Cumulative Return'] = (1 + LLY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LLYmean = LLY['returns'].mean()
LLYvar = LLY['returns'].var()
LLYkurt = LLY['returns'].kurt()
LLYskew = LLY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LLYbef = web.DataReader('LLY', 'yahoo', start, crashstart)
LLYbef['avgreturnbefore'] = LLYbef['Close'].pct_change(1)
LLYbef['Cumulative Return Before Crash'] = (1 + LLYbef['avgreturnbefore']).cumprod()

#period of crash
LLYdur = web.DataReader('LLY', 'yahoo', crashstart, crashend)
LLYdur['avgreturnduring'] = LLYdur['Close'].pct_change(1)
LLYdur['Cumulative Return During Crash'] = (1 + LLYdur['avgreturnduring']).cumprod()

#period after crash
LLYaft = web.DataReader('LLY', 'yahoo', crashend, end)
LLYaft['avgreturnafter'] = LLYaft['Close'].pct_change(1)
LLYaft['Cumulative Return After Crash'] = (1 + LLYaft['avgreturnafter']).cumprod()
In [395]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MCK = web.DataReader('MCK', 'yahoo', start, end)
MCK['returns'] = MCK['Close'].pct_change(1)
MCK['Cumulative Return'] = (1 + MCK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MCKmean = MCK['returns'].mean()
MCKvar = MCK['returns'].var()
MCKkurt = MCK['returns'].kurt()
MCKskew = MCK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MCKbef = web.DataReader('MCK', 'yahoo', start, crashstart)
MCKbef['avgreturnbefore'] = MCKbef['Close'].pct_change(1)
MCKbef['Cumulative Return Before Crash'] = (1 + MCKbef['avgreturnbefore']).cumprod()

#period of crash
MCKdur = web.DataReader('MCK', 'yahoo', crashstart, crashend)
MCKdur['avgreturnduring'] = MCKdur['Close'].pct_change(1)
MCKdur['Cumulative Return During Crash'] = (1 + MCKdur['avgreturnduring']).cumprod()

#period after crash
MCKaft = web.DataReader('MCK', 'yahoo', crashend, end)
MCKaft['avgreturnafter'] = MCKaft['Close'].pct_change(1)
MCKaft['Cumulative Return After Crash'] = (1 + MCKaft['avgreturnafter']).cumprod()
In [396]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MDT = web.DataReader('MDT', 'yahoo', start, end)
MDT['returns'] = MDT['Close'].pct_change(1)
MDT['Cumulative Return'] = (1 + MDT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MDTmean = MDT['returns'].mean()
MDTvar = MDT['returns'].var()
MDTkurt = MDT['returns'].kurt()
MDTskew = MDT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MDTbef = web.DataReader('MDT', 'yahoo', start, crashstart)
MDTbef['avgreturnbefore'] = MDTbef['Close'].pct_change(1)
MDTbef['Cumulative Return Before Crash'] = (1 + MDTbef['avgreturnbefore']).cumprod()

#period of crash
MDTdur = web.DataReader('MDT', 'yahoo', crashstart, crashend)
MDTdur['avgreturnduring'] = MDTdur['Close'].pct_change(1)
MDTdur['Cumulative Return During Crash'] = (1 + MDTdur['avgreturnduring']).cumprod()

#period after crash
MDTaft = web.DataReader('MDT', 'yahoo', crashend, end)
MDTaft['avgreturnafter'] = MDTaft['Close'].pct_change(1)
MDTaft['Cumulative Return After Crash'] = (1 + MDTaft['avgreturnafter']).cumprod()
In [397]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MRK = web.DataReader('MRK', 'yahoo', start, end)
MRK['returns'] = MRK['Close'].pct_change(1)
MRK['Cumulative Return'] = (1 + MRK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MRKmean = MRK['returns'].mean()
MRKvar = MRK['returns'].var()
MRKkurt = MRK['returns'].kurt()
MRKskew = MRK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MRKbef = web.DataReader('MRK', 'yahoo', start, crashstart)
MRKbef['avgreturnbefore'] = MRKbef['Close'].pct_change(1)
MRKbef['Cumulative Return Before Crash'] = (1 + MRKbef['avgreturnbefore']).cumprod()

#period of crash
MRKdur = web.DataReader('MRK', 'yahoo', crashstart, crashend)
MRKdur['avgreturnduring'] = MRKdur['Close'].pct_change(1)
MRKdur['Cumulative Return During Crash'] = (1 + MRKdur['avgreturnduring']).cumprod()

#period after crash
MRKaft = web.DataReader('MRK', 'yahoo', crashend, end)
MRKaft['avgreturnafter'] = MRKaft['Close'].pct_change(1)
MRKaft['Cumulative Return After Crash'] = (1 + MRKaft['avgreturnafter']).cumprod()
In [398]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MTD = web.DataReader('MTD', 'yahoo', start, end)
MTD['returns'] = MTD['Close'].pct_change(1)
MTD['Cumulative Return'] = (1 + MTD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MTDmean = MTD['returns'].mean()
MTDvar = MTD['returns'].var()
MTDkurt = MTD['returns'].kurt()
MTDskew = MTD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MTDbef = web.DataReader('MTD', 'yahoo', start, crashstart)
MTDbef['avgreturnbefore'] = MTDbef['Close'].pct_change(1)
MTDbef['Cumulative Return Before Crash'] = (1 + MTDbef['avgreturnbefore']).cumprod()

#period of crash
MTDdur = web.DataReader('MTD', 'yahoo', crashstart, crashend)
MTDdur['avgreturnduring'] = MTDdur['Close'].pct_change(1)
MTDdur['Cumulative Return During Crash'] = (1 + MTDdur['avgreturnduring']).cumprod()

#period after crash
MTDaft = web.DataReader('MTD', 'yahoo', crashend, end)
MTDaft['avgreturnafter'] = MTDaft['Close'].pct_change(1)
MTDaft['Cumulative Return After Crash'] = (1 + MTDaft['avgreturnafter']).cumprod()
In [399]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MYL = web.DataReader('MYL', 'yahoo', start, end)
MYL['returns'] = MYL['Close'].pct_change(1)
MYL['Cumulative Return'] = (1 + MYL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MYLmean = MYL['returns'].mean()
MYLvar = MYL['returns'].var()
MYLkurt = MYL['returns'].kurt()
MYLskew = MYL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MYLbef = web.DataReader('MYL', 'yahoo', start, crashstart)
MYLbef['avgreturnbefore'] = MYLbef['Close'].pct_change(1)
MYLbef['Cumulative Return Before Crash'] = (1 + MYLbef['avgreturnbefore']).cumprod()

#period of crash
MYLdur = web.DataReader('MYL', 'yahoo', crashstart, crashend)
MYLdur['avgreturnduring'] = MYLdur['Close'].pct_change(1)
MYLdur['Cumulative Return During Crash'] = (1 + MYLdur['avgreturnduring']).cumprod()

#period after crash
MYLaft = web.DataReader('MYL', 'yahoo', crashend, end)
MYLaft['avgreturnafter'] = MYLaft['Close'].pct_change(1)
MYLaft['Cumulative Return After Crash'] = (1 + MYLaft['avgreturnafter']).cumprod()
In [400]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PKI = web.DataReader('PKI', 'yahoo', start, end)
PKI['returns'] = PKI['Close'].pct_change(1)
PKI['Cumulative Return'] = (1 + PKI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PKImean = PKI['returns'].mean()
PKIvar = PKI['returns'].var()
PKIkurt = PKI['returns'].kurt()
PKIskew = PKI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PKIbef = web.DataReader('PKI', 'yahoo', start, crashstart)
PKIbef['avgreturnbefore'] = PKIbef['Close'].pct_change(1)
PKIbef['Cumulative Return Before Crash'] = (1 + PKIbef['avgreturnbefore']).cumprod()

#period of crash
PKIdur = web.DataReader('PKI', 'yahoo', crashstart, crashend)
PKIdur['avgreturnduring'] = PKIdur['Close'].pct_change(1)
PKIdur['Cumulative Return During Crash'] = (1 + PKIdur['avgreturnduring']).cumprod()

#period after crash
PKIaft = web.DataReader('PKI', 'yahoo', crashend, end)
PKIaft['avgreturnafter'] = PKIaft['Close'].pct_change(1)
PKIaft['Cumulative Return After Crash'] = (1 + PKIaft['avgreturnafter']).cumprod()
In [401]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PRGO = web.DataReader('PRGO', 'yahoo', start, end)
PRGO['returns'] = PRGO['Close'].pct_change(1)
PRGO['Cumulative Return'] = (1 + PRGO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PRGOmean = PRGO['returns'].mean()
PRGOvar = PRGO['returns'].var()
PRGOkurt = PRGO['returns'].kurt()
PRGOskew = PRGO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PRGObef = web.DataReader('PRGO', 'yahoo', start, crashstart)
PRGObef['avgreturnbefore'] = PRGObef['Close'].pct_change(1)
PRGObef['Cumulative Return Before Crash'] = (1 + PRGObef['avgreturnbefore']).cumprod()

#period of crash
PRGOdur = web.DataReader('PRGO', 'yahoo', crashstart, crashend)
PRGOdur['avgreturnduring'] = PRGOdur['Close'].pct_change(1)
PRGOdur['Cumulative Return During Crash'] = (1 + PRGOdur['avgreturnduring']).cumprod()

#period after crash
PRGOaft = web.DataReader('PRGO', 'yahoo', crashend, end)
PRGOaft['avgreturnafter'] = PRGOaft['Close'].pct_change(1)
PRGOaft['Cumulative Return After Crash'] = (1 + PRGOaft['avgreturnafter']).cumprod()
In [402]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PFE = web.DataReader('PFE', 'yahoo', start, end)
PFE['returns'] = PFE['Close'].pct_change(1)
PFE['Cumulative Return'] = (1 + PFE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PFEmean = PFE['returns'].mean()
PFEvar = PFE['returns'].var()
PFEkurt = PFE['returns'].kurt()
PFEskew = PFE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PFEbef = web.DataReader('PFE', 'yahoo', start, crashstart)
PFEbef['avgreturnbefore'] = PFEbef['Close'].pct_change(1)
PFEbef['Cumulative Return Before Crash'] = (1 + PFEbef['avgreturnbefore']).cumprod()

#period of crash
PFEdur = web.DataReader('PFE', 'yahoo', crashstart, crashend)
PFEdur['avgreturnduring'] = PFEdur['Close'].pct_change(1)
PFEdur['Cumulative Return During Crash'] = (1 + PFEdur['avgreturnduring']).cumprod()

#period after crash
PFEaft = web.DataReader('PFE', 'yahoo', crashend, end)
PFEaft['avgreturnafter'] = PFEaft['Close'].pct_change(1)
PFEaft['Cumulative Return After Crash'] = (1 + PFEaft['avgreturnafter']).cumprod()
In [403]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DGX = web.DataReader('DGX', 'yahoo', start, end)
DGX['returns'] = DGX['Close'].pct_change(1)
DGX['Cumulative Return'] = (1 + DGX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DGXmean = DGX['returns'].mean()
DGXvar = DGX['returns'].var()
DGXkurt = DGX['returns'].kurt()
DGXskew = DGX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DGXbef = web.DataReader('DGX', 'yahoo', start, crashstart)
DGXbef['avgreturnbefore'] = DGXbef['Close'].pct_change(1)
DGXbef['Cumulative Return Before Crash'] = (1 + DGXbef['avgreturnbefore']).cumprod()

#period of crash
DGXdur = web.DataReader('DGX', 'yahoo', crashstart, crashend)
DGXdur['avgreturnduring'] = DGXdur['Close'].pct_change(1)
DGXdur['Cumulative Return During Crash'] = (1 + DGXdur['avgreturnduring']).cumprod()

#period after crash
DGXaft = web.DataReader('DGX', 'yahoo', crashend, end)
DGXaft['avgreturnafter'] = DGXaft['Close'].pct_change(1)
DGXaft['Cumulative Return After Crash'] = (1 + DGXaft['avgreturnafter']).cumprod()
In [404]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
REGN = web.DataReader('REGN', 'yahoo', start, end)
REGN['returns'] = REGN['Close'].pct_change(1)
REGN['Cumulative Return'] = (1 + REGN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
REGNmean = REGN['returns'].mean()
REGNvar = REGN['returns'].var()
REGNkurt = REGN['returns'].kurt()
REGNskew = REGN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
REGNbef = web.DataReader('REGN', 'yahoo', start, crashstart)
REGNbef['avgreturnbefore'] = REGNbef['Close'].pct_change(1)
REGNbef['Cumulative Return Before Crash'] = (1 + REGNbef['avgreturnbefore']).cumprod()

#period of crash
REGNdur = web.DataReader('REGN', 'yahoo', crashstart, crashend)
REGNdur['avgreturnduring'] = REGNdur['Close'].pct_change(1)
REGNdur['Cumulative Return During Crash'] = (1 + REGNdur['avgreturnduring']).cumprod()

#period after crash
REGNaft = web.DataReader('REGN', 'yahoo', crashend, end)
REGNaft['avgreturnafter'] = REGNaft['Close'].pct_change(1)
REGNaft['Cumulative Return After Crash'] = (1 + REGNaft['avgreturnafter']).cumprod()
In [406]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
RMD = web.DataReader('RMD', 'yahoo', start, end)
RMD['returns'] = RMD['Close'].pct_change(1)
RMD['Cumulative Return'] = (1 + RMD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
RMDmean = RMD['returns'].mean()
RMDvar = RMD['returns'].var()
RMDkurt = RMD['returns'].kurt()
RMDskew = RMD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
RMDbef = web.DataReader('RMD', 'yahoo', start, crashstart)
RMDbef['avgreturnbefore'] = RMDbef['Close'].pct_change(1)
RMDbef['Cumulative Return Before Crash'] = (1 + RMDbef['avgreturnbefore']).cumprod()

#period of crash
RMDdur = web.DataReader('RMD', 'yahoo', crashstart, crashend)
RMDdur['avgreturnduring'] = RMDdur['Close'].pct_change(1)
RMDdur['Cumulative Return During Crash'] = (1 + RMDdur['avgreturnduring']).cumprod()

#period after crash
RMDaft = web.DataReader('RMD', 'yahoo', crashend, end)
RMDaft['avgreturnafter'] = RMDaft['Close'].pct_change(1)
RMDaft['Cumulative Return After Crash'] = (1 + RMDaft['avgreturnafter']).cumprod()
In [407]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
STE = web.DataReader('STE', 'yahoo', start, end)
STE['returns'] = STE['Close'].pct_change(1)
STE['Cumulative Return'] = (1 + STE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
STEmean = STE['returns'].mean()
STEvar = STE['returns'].var()
STEkurt = STE['returns'].kurt()
STEskew = STE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
STEbef = web.DataReader('STE', 'yahoo', start, crashstart)
STEbef['avgreturnbefore'] = STEbef['Close'].pct_change(1)
STEbef['Cumulative Return Before Crash'] = (1 + STEbef['avgreturnbefore']).cumprod()

#period of crash
STEdur = web.DataReader('STE', 'yahoo', crashstart, crashend)
STEdur['avgreturnduring'] = STEdur['Close'].pct_change(1)
STEdur['Cumulative Return During Crash'] = (1 + STEdur['avgreturnduring']).cumprod()

#period after crash
STEaft = web.DataReader('STE', 'yahoo', crashend, end)
STEaft['avgreturnafter'] = STEaft['Close'].pct_change(1)
STEaft['Cumulative Return After Crash'] = (1 + STEaft['avgreturnafter']).cumprod()
In [408]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SYK = web.DataReader('SYK', 'yahoo', start, end)
SYK['returns'] = SYK['Close'].pct_change(1)
SYK['Cumulative Return'] = (1 + SYK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SYKmean = SYK['returns'].mean()
SYKvar = SYK['returns'].var()
SYKkurt = SYK['returns'].kurt()
SYKskew = SYK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SYKbef = web.DataReader('SYK', 'yahoo', start, crashstart)
SYKbef['avgreturnbefore'] = SYKbef['Close'].pct_change(1)
SYKbef['Cumulative Return Before Crash'] = (1 + SYKbef['avgreturnbefore']).cumprod()

#period of crash
SYKdur = web.DataReader('SYK', 'yahoo', crashstart, crashend)
SYKdur['avgreturnduring'] = SYKdur['Close'].pct_change(1)
SYKdur['Cumulative Return During Crash'] = (1 + SYKdur['avgreturnduring']).cumprod()

#period after crash
SYKaft = web.DataReader('SYK', 'yahoo', crashend, end)
SYKaft['avgreturnafter'] = SYKaft['Close'].pct_change(1)
SYKaft['Cumulative Return After Crash'] = (1 + SYKaft['avgreturnafter']).cumprod()
In [409]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TFX = web.DataReader('TFX', 'yahoo', start, end)
TFX['returns'] = TFX['Close'].pct_change(1)
TFX['Cumulative Return'] = (1 + TFX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TFXmean = TFX['returns'].mean()
TFXvar = TFX['returns'].var()
TFXkurt = TFX['returns'].kurt()
TFXskew = TFX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TFXbef = web.DataReader('TFX', 'yahoo', start, crashstart)
TFXbef['avgreturnbefore'] = TFXbef['Close'].pct_change(1)
TFXbef['Cumulative Return Before Crash'] = (1 + TFXbef['avgreturnbefore']).cumprod()

#period of crash
TFXdur = web.DataReader('TFX', 'yahoo', crashstart, crashend)
TFXdur['avgreturnduring'] = TFXdur['Close'].pct_change(1)
TFXdur['Cumulative Return During Crash'] = (1 + TFXdur['avgreturnduring']).cumprod()

#period after crash
TFXaft = web.DataReader('TFX', 'yahoo', crashend, end)
TFXaft['avgreturnafter'] = TFXaft['Close'].pct_change(1)
TFXaft['Cumulative Return After Crash'] = (1 + TFXaft['avgreturnafter']).cumprod()
In [410]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TMO = web.DataReader('TMO', 'yahoo', start, end)
TMO['returns'] = TMO['Close'].pct_change(1)
TMO['Cumulative Return'] = (1 + TMO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TMOmean = TMO['returns'].mean()
TMOvar = TMO['returns'].var()
TMOkurt = TMO['returns'].kurt()
TMOskew = TMO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TMObef = web.DataReader('TMO', 'yahoo', start, crashstart)
TMObef['avgreturnbefore'] = TMObef['Close'].pct_change(1)
TMObef['Cumulative Return Before Crash'] = (1 + TMObef['avgreturnbefore']).cumprod()

#period of crash
TMOdur = web.DataReader('TMO', 'yahoo', crashstart, crashend)
TMOdur['avgreturnduring'] = TMOdur['Close'].pct_change(1)
TMOdur['Cumulative Return During Crash'] = (1 + TMOdur['avgreturnduring']).cumprod()

#period after crash
TMOaft = web.DataReader('TMO', 'yahoo', crashend, end)
TMOaft['avgreturnafter'] = TMOaft['Close'].pct_change(1)
TMOaft['Cumulative Return After Crash'] = (1 + TMOaft['avgreturnafter']).cumprod()
In [411]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
UNH = web.DataReader('UNH', 'yahoo', start, end)
UNH['returns'] = UNH['Close'].pct_change(1)
UNH['Cumulative Return'] = (1 + UNH['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
UNHmean = UNH['returns'].mean()
UNHvar = UNH['returns'].var()
UNHkurt = UNH['returns'].kurt()
UNHskew = UNH['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
UNHbef = web.DataReader('UNH', 'yahoo', start, crashstart)
UNHbef['avgreturnbefore'] = UNHbef['Close'].pct_change(1)
UNHbef['Cumulative Return Before Crash'] = (1 + UNHbef['avgreturnbefore']).cumprod()

#period of crash
UNHdur = web.DataReader('UNH', 'yahoo', crashstart, crashend)
UNHdur['avgreturnduring'] = UNHdur['Close'].pct_change(1)
UNHdur['Cumulative Return During Crash'] = (1 + UNHdur['avgreturnduring']).cumprod()

#period after crash
UNHaft = web.DataReader('UNH', 'yahoo', crashend, end)
UNHaft['avgreturnafter'] = UNHaft['Close'].pct_change(1)
UNHaft['Cumulative Return After Crash'] = (1 + UNHaft['avgreturnafter']).cumprod()
In [412]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
UHS = web.DataReader('UHS', 'yahoo', start, end)
UHS['returns'] = UHS['Close'].pct_change(1)
UHS['Cumulative Return'] = (1 + UHS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
UHSmean = UHS['returns'].mean()
UHSvar = UHS['returns'].var()
UHSkurt = UHS['returns'].kurt()
UHSskew = UHS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
UHSbef = web.DataReader('UHS', 'yahoo', start, crashstart)
UHSbef['avgreturnbefore'] = UHSbef['Close'].pct_change(1)
UHSbef['Cumulative Return Before Crash'] = (1 + UHSbef['avgreturnbefore']).cumprod()

#period of crash
UHSdur = web.DataReader('UHS', 'yahoo', crashstart, crashend)
UHSdur['avgreturnduring'] = UHSdur['Close'].pct_change(1)
UHSdur['Cumulative Return During Crash'] = (1 + UHSdur['avgreturnduring']).cumprod()

#period after crash
UHSaft = web.DataReader('UHS', 'yahoo', crashend, end)
UHSaft['avgreturnafter'] = UHSaft['Close'].pct_change(1)
UHSaft['Cumulative Return After Crash'] = (1 + UHSaft['avgreturnafter']).cumprod()
In [413]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
VAR = web.DataReader('VAR', 'yahoo', start, end)
VAR['returns'] = VAR['Close'].pct_change(1)
VAR['Cumulative Return'] = (1 + VAR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
VARmean = VAR['returns'].mean()
VARvar = VAR['returns'].var()
VARkurt = VAR['returns'].kurt()
VARskew = VAR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
VARbef = web.DataReader('VAR', 'yahoo', start, crashstart)
VARbef['avgreturnbefore'] = VARbef['Close'].pct_change(1)
VARbef['Cumulative Return Before Crash'] = (1 + VARbef['avgreturnbefore']).cumprod()

#period of crash
VARdur = web.DataReader('VAR', 'yahoo', crashstart, crashend)
VARdur['avgreturnduring'] = VARdur['Close'].pct_change(1)
VARdur['Cumulative Return During Crash'] = (1 + VARdur['avgreturnduring']).cumprod()

#period after crash
VARaft = web.DataReader('VAR', 'yahoo', crashend, end)
VARaft['avgreturnafter'] = VARaft['Close'].pct_change(1)
VARaft['Cumulative Return After Crash'] = (1 + VARaft['avgreturnafter']).cumprod()
In [414]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
VRTX = web.DataReader('VRTX', 'yahoo', start, end)
VRTX['returns'] = VRTX['Close'].pct_change(1)
VRTX['Cumulative Return'] = (1 + VRTX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
VRTXmean = VRTX['returns'].mean()
VRTXvar = VRTX['returns'].var()
VRTXkurt = VRTX['returns'].kurt()
VRTXskew = VRTX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
VRTXbef = web.DataReader('VRTX', 'yahoo', start, crashstart)
VRTXbef['avgreturnbefore'] = VRTXbef['Close'].pct_change(1)
VRTXbef['Cumulative Return Before Crash'] = (1 + VRTXbef['avgreturnbefore']).cumprod()

#period of crash
VRTXdur = web.DataReader('VRTX', 'yahoo', crashstart, crashend)
VRTXdur['avgreturnduring'] = VRTXdur['Close'].pct_change(1)
VRTXdur['Cumulative Return During Crash'] = (1 + VRTXdur['avgreturnduring']).cumprod()

#period after crash
VRTXaft = web.DataReader('VRTX', 'yahoo', crashend, end)
VRTXaft['avgreturnafter'] = VRTXaft['Close'].pct_change(1)
VRTXaft['Cumulative Return After Crash'] = (1 + VRTXaft['avgreturnafter']).cumprod()
In [415]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WAT = web.DataReader('WAT', 'yahoo', start, end)
WAT['returns'] = WAT['Close'].pct_change(1)
WAT['Cumulative Return'] = (1 + WAT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WATmean = WAT['returns'].mean()
WATvar = WAT['returns'].var()
WATkurt = WAT['returns'].kurt()
WATskew = WAT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WATbef = web.DataReader('WAT', 'yahoo', start, crashstart)
WATbef['avgreturnbefore'] = WATbef['Close'].pct_change(1)
WATbef['Cumulative Return Before Crash'] = (1 + WATbef['avgreturnbefore']).cumprod()

#period of crash
WATdur = web.DataReader('WAT', 'yahoo', crashstart, crashend)
WATdur['avgreturnduring'] = WATdur['Close'].pct_change(1)
WATdur['Cumulative Return During Crash'] = (1 + WATdur['avgreturnduring']).cumprod()

#period after crash
WATaft = web.DataReader('WAT', 'yahoo', crashend, end)
WATaft['avgreturnafter'] = WATaft['Close'].pct_change(1)
WATaft['Cumulative Return After Crash'] = (1 + WATaft['avgreturnafter']).cumprod()
In [416]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WST = web.DataReader('WST', 'yahoo', start, end)
WST['returns'] = WST['Close'].pct_change(1)
WST['Cumulative Return'] = (1 + WST['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WSTmean = WST['returns'].mean()
WSTvar = WST['returns'].var()
WSTkurt = WST['returns'].kurt()
WSTskew = WST['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WSTbef = web.DataReader('WST', 'yahoo', start, crashstart)
WSTbef['avgreturnbefore'] = WSTbef['Close'].pct_change(1)
WSTbef['Cumulative Return Before Crash'] = (1 + WSTbef['avgreturnbefore']).cumprod()

#period of crash
WSTdur = web.DataReader('WST', 'yahoo', crashstart, crashend)
WSTdur['avgreturnduring'] = WSTdur['Close'].pct_change(1)
WSTdur['Cumulative Return During Crash'] = (1 + WSTdur['avgreturnduring']).cumprod()

#period after crash
WSTaft = web.DataReader('WST', 'yahoo', crashend, end)
WSTaft['avgreturnafter'] = WSTaft['Close'].pct_change(1)
WSTaft['Cumulative Return After Crash'] = (1 + WSTaft['avgreturnafter']).cumprod()
In [417]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ZBH = web.DataReader('ZBH', 'yahoo', start, end)
ZBH['returns'] = ZBH['Close'].pct_change(1)
ZBH['Cumulative Return'] = (1 + ZBH['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ZBHmean = ZBH['returns'].mean()
ZBHvar = ZBH['returns'].var()
ZBHkurt = ZBH['returns'].kurt()
ZBHskew = ZBH['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ZBHbef = web.DataReader('ZBH', 'yahoo', start, crashstart)
ZBHbef['avgreturnbefore'] = ZBHbef['Close'].pct_change(1)
ZBHbef['Cumulative Return Before Crash'] = (1 + ZBHbef['avgreturnbefore']).cumprod()

#period of crash
ZBHdur = web.DataReader('ZBH', 'yahoo', crashstart, crashend)
ZBHdur['avgreturnduring'] = ZBHdur['Close'].pct_change(1)
ZBHdur['Cumulative Return During Crash'] = (1 + ZBHdur['avgreturnduring']).cumprod()

#period after crash
ZBHaft = web.DataReader('ZBH', 'yahoo', crashend, end)
ZBHaft['avgreturnafter'] = ZBHaft['Close'].pct_change(1)
ZBHaft['Cumulative Return After Crash'] = (1 + ZBHaft['avgreturnafter']).cumprod()
In [418]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ZTS = web.DataReader('ZTS', 'yahoo', start, end)
ZTS['returns'] = ZTS['Close'].pct_change(1)
ZTS['Cumulative Return'] = (1 + ZTS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ZTSmean = ZTS['returns'].mean()
ZTSvar = ZTS['returns'].var()
ZTSkurt = ZTS['returns'].kurt()
ZTSskew = ZTS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ZTSbef = web.DataReader('ZTS', 'yahoo', start, crashstart)
ZTSbef['avgreturnbefore'] = ZTSbef['Close'].pct_change(1)
ZTSbef['Cumulative Return Before Crash'] = (1 + ZTSbef['avgreturnbefore']).cumprod()

#period of crash
ZTSdur = web.DataReader('ZTS', 'yahoo', crashstart, crashend)
ZTSdur['avgreturnduring'] = ZTSdur['Close'].pct_change(1)
ZTSdur['Cumulative Return During Crash'] = (1 + ZTSdur['avgreturnduring']).cumprod()

#period after crash
ZTSaft = web.DataReader('ZTS', 'yahoo', crashend, end)
ZTSaft['avgreturnafter'] = ZTSaft['Close'].pct_change(1)
ZTSaft['Cumulative Return After Crash'] = (1 + ZTSaft['avgreturnafter']).cumprod()
In [419]:
#KDE Plot for Healthcare Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
ABT['returns'].plot(kind = 'kde', label ='ABT', figsize = (10,8), alpha=0.5, title = 'KDE of Healthcare Stocks')
ABBV['returns'].plot(kind = 'kde', label ='ABBV', alpha=0.5)
ABMD['returns'].plot(kind = 'kde', label ='ABMD', alpha=0.5)
A['returns'].plot(kind = 'kde', label ='A', alpha=0.5)
ALXN['returns'].plot(kind = 'kde', label ='ALXN', alpha=0.5)
ALGN['returns'].plot(kind = 'kde', label ='ALGN', alpha=0.5)
ABC['returns'].plot(kind = 'kde', label ='ABC', alpha=0.5)
AMGN['returns'].plot(kind = 'kde', label ='AMGN', alpha=0.5)
ANTM['returns'].plot(kind = 'kde', label ='ANTM', alpha=0.5)
BAX['returns'].plot(kind = 'kde', label ='BAX', alpha=0.5)
BDX['returns'].plot(kind = 'kde', label ='BDX', alpha=0.5)
BIIB['returns'].plot(kind = 'kde', label ='BIIB', alpha=0.5)
BSX['returns'].plot(kind = 'kde', label ='BSX', alpha=0.5)
BMY['returns'].plot(kind = 'kde', label ='BMY', alpha=0.5)
CAH['returns'].plot(kind = 'kde', label = 'CAH', alpha=0.5)
CNC['returns'].plot(kind = 'kde', label ='CNC', alpha=0.5)
CERN['returns'].plot(kind = 'kde', label ='CERN', alpha=0.5)
CI['returns'].plot(kind = 'kde', label ='CI', alpha=0.5)
COO['returns'].plot(kind = 'kde', label ='COO', alpha=0.5)
CVS['returns'].plot(kind = 'kde', label ='CVS', alpha=0.5)
DHR['returns'].plot(kind = 'kde', label ='DHR', alpha=0.5)
DVA['returns'].plot(kind = 'kde', label ='DVA', alpha=0.5)
XRAY['returns'].plot(kind = 'kde', label ='XRAY', alpha=0.5)
DXCM['returns'].plot(kind = 'kde', label ='DXCM', alpha=0.5)
EW['returns'].plot(kind = 'kde', label ='EW', alpha=0.5)
GILD['returns'].plot(kind = 'kde', label ='GILD', alpha=0.5)
HCA['returns'].plot(kind = 'kde', label ='HCA', alpha=0.5)
HSIC['returns'].plot(kind = 'kde', label ='HSIC', alpha=0.5)
HOLX['returns'].plot(kind = 'kde', label ='HOLX', alpha=0.5)
HUM['returns'].plot(kind = 'kde', label ='HUM', alpha=0.5)
IDXX['returns'].plot(kind = 'kde', label ='IDXX', alpha=0.5)
ILMN['returns'].plot(kind = 'kde', label ='ILMN', alpha=0.5)
INCY['returns'].plot(kind = 'kde', label ='INCY', alpha=0.5)
ISRG['returns'].plot(kind = 'kde', label ='ISRG', alpha=0.5)
IQV['returns'].plot(kind = 'kde', label ='IQV', alpha=0.5)
JNJ['returns'].plot(kind = 'kde', label ='JNJ', alpha=0.5)
LH['returns'].plot(kind = 'kde', label ='LH', alpha=0.5)
LLY['returns'].plot(kind = 'kde', label ='LLY', alpha=0.5)
MCK['returns'].plot(kind = 'kde', label ='MCK', alpha=0.5)
MDT['returns'].plot(kind = 'kde', label ='MDT', alpha=0.5)
MRK['returns'].plot(kind = 'kde', label ='MRK', alpha=0.5)
MTD['returns'].plot(kind = 'kde', label ='MTD', alpha=0.5)
MYL['returns'].plot(kind = 'kde', label ='MYL', alpha=0.5)
PKI['returns'].plot(kind = 'kde', label ='PKI', alpha=0.5)
PRGO['returns'].plot(kind = 'kde', label ='PRGO', alpha=0.5)
PFE['returns'].plot(kind = 'kde', label ='PFE', alpha=0.5)
DGX['returns'].plot(kind = 'kde', label ='DGX', alpha=0.5)
REGN['returns'].plot(kind = 'kde', label ='REGN', alpha=0.5)
RMD['returns'].plot(kind = 'kde', label ='RMD', alpha=0.5)
STE['returns'].plot(kind = 'kde', label ='STE', alpha=0.5)
SYK['returns'].plot(kind = 'kde', label ='SYK', alpha=0.5)
TFX['returns'].plot(kind = 'kde', label ='TFX', alpha=0.5)
TMO['returns'].plot(kind = 'kde', label ='TMO', alpha=0.5)
UNH['returns'].plot(kind = 'kde', label ='UNH', alpha=0.5)
UHS['returns'].plot(kind = 'kde', label ='UHS', alpha=0.5)
VAR['returns'].plot(kind = 'kde', label ='VAR', alpha=0.5)
VRTX['returns'].plot(kind = 'kde', label ='VRTX', alpha=0.5)
WAT['returns'].plot(kind = 'kde', label ='WAT', alpha=0.5)
WST['returns'].plot(kind = 'kde', label ='WST', alpha=0.5)
ZBH['returns'].plot(kind = 'kde', label ='ZBH', alpha=0.5)
ZTS['returns'].plot(kind = 'kde', label ='ZTS', alpha=0.5)

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [420]:
#Boxplots comparing returns for Healthcare Sector
box_df = pd.concat([sp['returns'],ABT['returns'],ABBV['returns'],ABMD['returns'],A['returns'],ALXN['returns'],ALGN['returns'],ABC['returns'],AMGN['returns'],ANTM['returns'],BAX['returns'],BDX['returns'],BIIB['returns'],BSX['returns'],BMY['returns'],CAH['returns'],CNC['returns'],CERN['returns'],CI['returns'],COO['returns'],CVS['returns'],DHR['returns'],DVA['returns'],XRAY['returns'],DXCM['returns'],EW['returns'],GILD['returns'],HCA['returns'],HSIC['returns'],HOLX['returns'],HUM['returns'],IDXX['returns'],ILMN['returns'],INCY['returns'],ISRG['returns'],IQV['returns'],JNJ['returns'],LH['returns'],LLY['returns'],MCK['returns'],MDT['returns'],MRK['returns'],MTD['returns'],MYL['returns'],PKI['returns'],PRGO['returns'],PFE['returns'],DGX['returns'],REGN['returns'],RMD['returns'],STE['returns'],SYK['returns'],TFX['returns'],TMO['returns'],UNH['returns'],UHS['returns'],VAR['returns'],VRTX['returns'],WAT['returns'],WST['returns'],ZBH['returns'],ZTS['returns']], axis=1)
box_df.columns = ['S&P 500','ABT','ABBV','ABMD','A','ALXN','ALGN','ABC','AMGN','ANTM','BAX','BDX','BIIB','BSX','BMY','CAH','CNC','CERN','CI','COO','CVS','DHR','DVA','XRAY','DXCM','EW','GILD','HCA','HSIC','HOLX','HUM','IDXX','ILMN','INCY','ISRG','IQV','JNJ','LH','LLY','MCK','MDT','MRK','MTD','MYL','PKI','PRGO','PFE','DGX','REGN','RMD','STE','SYK','TFX','TMO','UNH','UHS','VAR','VRTX','WAT','WST','ZBH','ZTS']
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Healthcare Sector Returns')
Out[420]:
<matplotlib.axes._subplots.AxesSubplot at 0x13fa090f400>
In [421]:
#Cumulative Returns of Healthcare Stocks for Whole period

sp['Cumulative Return'].plot(label="S&P 500")
ABT['Cumulative Return'].plot(label='ABT', figsize=(16,8), title = 'Cumulative Return of Healthcare Stocks')
ABBV['Cumulative Return'].plot(label="ABBV")
ABMD['Cumulative Return'].plot(label="ABMD")
A['Cumulative Return'].plot(label="A")
ALXN['Cumulative Return'].plot(label="ALXN")
ALGN['Cumulative Return'].plot(label="ALGN")
ABC['Cumulative Return'].plot(label="ABC")
AMGN['Cumulative Return'].plot(label="AMGN")
ANTM['Cumulative Return'].plot(label="ANTM")
BAX['Cumulative Return'].plot(label="BAX")
BDX['Cumulative Return'].plot(label="BDX")
BIIB['Cumulative Return'].plot(label="BIIB")
BSX['Cumulative Return'].plot(label="BSX")
BMY['Cumulative Return'].plot(label="BMY")
CAH['Cumulative Return'].plot(label="CAH")
CNC['Cumulative Return'].plot(label="CNC")
CERN['Cumulative Return'].plot(label="CERN")
CI['Cumulative Return'].plot(label="CI")
COO['Cumulative Return'].plot(label="COO")
CVS['Cumulative Return'].plot(label="CVS")
DHR['Cumulative Return'].plot(label="DHR")
DVA['Cumulative Return'].plot(label="DVA")
XRAY['Cumulative Return'].plot(label="XRAY")
DXCM['Cumulative Return'].plot(label="DXCM")
EW['Cumulative Return'].plot(label="EW")
GILD['Cumulative Return'].plot(label="GILD")
HCA['Cumulative Return'].plot(label="HCA")
HSIC['Cumulative Return'].plot(label="HSIC")
HOLX['Cumulative Return'].plot(label="HOLX")
HUM['Cumulative Return'].plot(label="HUM")
IDXX['Cumulative Return'].plot(label="IDXX")
ILMN['Cumulative Return'].plot(label="ILMN")
INCY['Cumulative Return'].plot(label="INCY")
ISRG['Cumulative Return'].plot(label="ISRG")
IQV['Cumulative Return'].plot(label="IQV")
JNJ['Cumulative Return'].plot(label="JNJ")
LH['Cumulative Return'].plot(label="LH")
LLY['Cumulative Return'].plot(label="LLY")
MCK['Cumulative Return'].plot(label="MCK")
MDT['Cumulative Return'].plot(label="MDT")
MRK['Cumulative Return'].plot(label="MRK")
MTD['Cumulative Return'].plot(label="MTD")
MYL['Cumulative Return'].plot(label="MYL")
PKI['Cumulative Return'].plot(label="PKI")
PRGO['Cumulative Return'].plot(label="PRGO")
PFE['Cumulative Return'].plot(label="PFE")
DGX['Cumulative Return'].plot(label="DGX")
REGN['Cumulative Return'].plot(label="REGN")
RMD['Cumulative Return'].plot(label="RMD")
STE['Cumulative Return'].plot(label="STE")
SYK['Cumulative Return'].plot(label="SYK")
TFX['Cumulative Return'].plot(label="TFX")
TMO['Cumulative Return'].plot(label="TMO")
UNH['Cumulative Return'].plot(label="UNH")
UHS['Cumulative Return'].plot(label="UHS")
VAR['Cumulative Return'].plot(label="VAR")
VRTX['Cumulative Return'].plot(label="VRTX")
WAT['Cumulative Return'].plot(label="WAT")
WST['Cumulative Return'].plot(label="WST")
ZBH['Cumulative Return'].plot(label="ZBH")
ZTS['Cumulative Return'].plot(label="ZTS")

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [422]:
#Sorting by Cumulative Return

cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],ABT['Cumulative Return'].iloc[-1],ABBV['Cumulative Return'].iloc[-1],ABMD['Cumulative Return'].iloc[-1],A['Cumulative Return'].iloc[-1],ALXN['Cumulative Return'].iloc[-1],ALGN['Cumulative Return'].iloc[-1],ABC['Cumulative Return'].iloc[-1],AMGN['Cumulative Return'].iloc[-1],ANTM['Cumulative Return'].iloc[-1],BAX['Cumulative Return'].iloc[-1],BDX['Cumulative Return'].iloc[-1],BIIB['Cumulative Return'].iloc[-1],BSX['Cumulative Return'].iloc[-1],BMY['Cumulative Return'].iloc[-1],CAH['Cumulative Return'].iloc[-1],CNC['Cumulative Return'].iloc[-1],CERN['Cumulative Return'].iloc[-1],CI['Cumulative Return'].iloc[-1],COO['Cumulative Return'].iloc[-1],CVS['Cumulative Return'].iloc[-1],DHR['Cumulative Return'].iloc[-1],DVA['Cumulative Return'].iloc[-1],XRAY['Cumulative Return'].iloc[-1],DXCM['Cumulative Return'].iloc[-1],EW['Cumulative Return'].iloc[-1],GILD['Cumulative Return'].iloc[-1],HCA['Cumulative Return'].iloc[-1],HSIC['Cumulative Return'].iloc[-1],HOLX['Cumulative Return'].iloc[-1],HUM['Cumulative Return'].iloc[-1],IDXX['Cumulative Return'].iloc[-1],ILMN['Cumulative Return'].iloc[-1],INCY['Cumulative Return'].iloc[-1],ISRG['Cumulative Return'].iloc[-1],IQV['Cumulative Return'].iloc[-1],JNJ['Cumulative Return'].iloc[-1],LH['Cumulative Return'].iloc[-1],LLY['Cumulative Return'].iloc[-1],MCK['Cumulative Return'].iloc[-1],MDT['Cumulative Return'].iloc[-1],MRK['Cumulative Return'].iloc[-1],MTD['Cumulative Return'].iloc[-1],MYL['Cumulative Return'].iloc[-1],PKI['Cumulative Return'].iloc[-1],PRGO['Cumulative Return'].iloc[-1],PFE['Cumulative Return'].iloc[-1],DGX['Cumulative Return'].iloc[-1],REGN['Cumulative Return'].iloc[-1],RMD['Cumulative Return'].iloc[-1],STE['Cumulative Return'].iloc[-1],SYK['Cumulative Return'].iloc[-1],TFX['Cumulative Return'].iloc[-1],TMO['Cumulative Return'].iloc[-1],UNH['Cumulative Return'].iloc[-1],UHS['Cumulative Return'].iloc[-1],VAR['Cumulative Return'].iloc[-1],VRTX['Cumulative Return'].iloc[-1],WAT['Cumulative Return'].iloc[-1],WST['Cumulative Return'].iloc[-1],ZBH['Cumulative Return'].iloc[-1],ZTS['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],ABTbef['Cumulative Return Before Crash'].iloc[-1],ABBVbef['Cumulative Return Before Crash'].iloc[-1],ABMDbef['Cumulative Return Before Crash'].iloc[-1],Abef['Cumulative Return Before Crash'].iloc[-1],ALXNbef['Cumulative Return Before Crash'].iloc[-1],ALGNbef['Cumulative Return Before Crash'].iloc[-1],ABCbef['Cumulative Return Before Crash'].iloc[-1],AMGNbef['Cumulative Return Before Crash'].iloc[-1],ANTMbef['Cumulative Return Before Crash'].iloc[-1],BAXbef['Cumulative Return Before Crash'].iloc[-1],BDXbef['Cumulative Return Before Crash'].iloc[-1],BIIBbef['Cumulative Return Before Crash'].iloc[-1],BSXbef['Cumulative Return Before Crash'].iloc[-1],BMYbef['Cumulative Return Before Crash'].iloc[-1],CAHbef['Cumulative Return Before Crash'].iloc[-1],CNCbef['Cumulative Return Before Crash'].iloc[-1],CERNbef['Cumulative Return Before Crash'].iloc[-1],CIbef['Cumulative Return Before Crash'].iloc[-1],COObef['Cumulative Return Before Crash'].iloc[-1],CVSbef['Cumulative Return Before Crash'].iloc[-1],DHRbef['Cumulative Return Before Crash'].iloc[-1],DVAbef['Cumulative Return Before Crash'].iloc[-1],XRAYbef['Cumulative Return Before Crash'].iloc[-1],DXCMbef['Cumulative Return Before Crash'].iloc[-1],EWbef['Cumulative Return Before Crash'].iloc[-1],GILDbef['Cumulative Return Before Crash'].iloc[-1],HCAbef['Cumulative Return Before Crash'].iloc[-1],HSICbef['Cumulative Return Before Crash'].iloc[-1],HOLXbef['Cumulative Return Before Crash'].iloc[-1],HUMbef['Cumulative Return Before Crash'].iloc[-1],IDXXbef['Cumulative Return Before Crash'].iloc[-1],ILMNbef['Cumulative Return Before Crash'].iloc[-1],INCYbef['Cumulative Return Before Crash'].iloc[-1],ISRGbef['Cumulative Return Before Crash'].iloc[-1],IQVbef['Cumulative Return Before Crash'].iloc[-1],JNJbef['Cumulative Return Before Crash'].iloc[-1],LHbef['Cumulative Return Before Crash'].iloc[-1],LLYbef['Cumulative Return Before Crash'].iloc[-1],MCKbef['Cumulative Return Before Crash'].iloc[-1],MDTbef['Cumulative Return Before Crash'].iloc[-1],MRKbef['Cumulative Return Before Crash'].iloc[-1],MTDbef['Cumulative Return Before Crash'].iloc[-1],MYLbef['Cumulative Return Before Crash'].iloc[-1],PKIbef['Cumulative Return Before Crash'].iloc[-1],PRGObef['Cumulative Return Before Crash'].iloc[-1],PFEbef['Cumulative Return Before Crash'].iloc[-1],DGXbef['Cumulative Return Before Crash'].iloc[-1],REGNbef['Cumulative Return Before Crash'].iloc[-1],RMDbef['Cumulative Return Before Crash'].iloc[-1],STEbef['Cumulative Return Before Crash'].iloc[-1],SYKbef['Cumulative Return Before Crash'].iloc[-1],TFXbef['Cumulative Return Before Crash'].iloc[-1],TMObef['Cumulative Return Before Crash'].iloc[-1],UNHbef['Cumulative Return Before Crash'].iloc[-1],UHSbef['Cumulative Return Before Crash'].iloc[-1],VARbef['Cumulative Return Before Crash'].iloc[-1],VRTXbef['Cumulative Return Before Crash'].iloc[-1],WATbef['Cumulative Return Before Crash'].iloc[-1],WSTbef['Cumulative Return Before Crash'].iloc[-1],ZBHbef['Cumulative Return Before Crash'].iloc[-1],ZTSbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],ABTdur['Cumulative Return During Crash'].iloc[-1],ABBVdur['Cumulative Return During Crash'].iloc[-1],ABMDdur['Cumulative Return During Crash'].iloc[-1],Adur['Cumulative Return During Crash'].iloc[-1],ALXNdur['Cumulative Return During Crash'].iloc[-1],ALGNdur['Cumulative Return During Crash'].iloc[-1],ABCdur['Cumulative Return During Crash'].iloc[-1],AMGNdur['Cumulative Return During Crash'].iloc[-1],ANTMdur['Cumulative Return During Crash'].iloc[-1],BAXdur['Cumulative Return During Crash'].iloc[-1],BDXdur['Cumulative Return During Crash'].iloc[-1],BIIBdur['Cumulative Return During Crash'].iloc[-1],BSXdur['Cumulative Return During Crash'].iloc[-1],BMYdur['Cumulative Return During Crash'].iloc[-1],CAHdur['Cumulative Return During Crash'].iloc[-1],CNCdur['Cumulative Return During Crash'].iloc[-1],CERNdur['Cumulative Return During Crash'].iloc[-1],CIdur['Cumulative Return During Crash'].iloc[-1],COOdur['Cumulative Return During Crash'].iloc[-1],CVSdur['Cumulative Return During Crash'].iloc[-1],DHRdur['Cumulative Return During Crash'].iloc[-1],DVAdur['Cumulative Return During Crash'].iloc[-1],XRAYdur['Cumulative Return During Crash'].iloc[-1],DXCMdur['Cumulative Return During Crash'].iloc[-1],EWdur['Cumulative Return During Crash'].iloc[-1],GILDdur['Cumulative Return During Crash'].iloc[-1],HCAdur['Cumulative Return During Crash'].iloc[-1],HSICdur['Cumulative Return During Crash'].iloc[-1],HOLXdur['Cumulative Return During Crash'].iloc[-1],HUMdur['Cumulative Return During Crash'].iloc[-1],IDXXdur['Cumulative Return During Crash'].iloc[-1],ILMNdur['Cumulative Return During Crash'].iloc[-1],INCYdur['Cumulative Return During Crash'].iloc[-1],ISRGdur['Cumulative Return During Crash'].iloc[-1],IQVdur['Cumulative Return During Crash'].iloc[-1],JNJdur['Cumulative Return During Crash'].iloc[-1],LHdur['Cumulative Return During Crash'].iloc[-1],LLYdur['Cumulative Return During Crash'].iloc[-1],MCKdur['Cumulative Return During Crash'].iloc[-1],MDTdur['Cumulative Return During Crash'].iloc[-1],MRKdur['Cumulative Return During Crash'].iloc[-1],MTDdur['Cumulative Return During Crash'].iloc[-1],MYLdur['Cumulative Return During Crash'].iloc[-1],PKIdur['Cumulative Return During Crash'].iloc[-1],PRGOdur['Cumulative Return During Crash'].iloc[-1],PFEdur['Cumulative Return During Crash'].iloc[-1],DGXdur['Cumulative Return During Crash'].iloc[-1],REGNdur['Cumulative Return During Crash'].iloc[-1],RMDdur['Cumulative Return During Crash'].iloc[-1],STEdur['Cumulative Return During Crash'].iloc[-1],SYKdur['Cumulative Return During Crash'].iloc[-1],TFXdur['Cumulative Return During Crash'].iloc[-1],TMOdur['Cumulative Return During Crash'].iloc[-1],UNHdur['Cumulative Return During Crash'].iloc[-1],UHSdur['Cumulative Return During Crash'].iloc[-1],VARdur['Cumulative Return During Crash'].iloc[-1],VRTXdur['Cumulative Return During Crash'].iloc[-1],WATdur['Cumulative Return During Crash'].iloc[-1],WSTdur['Cumulative Return During Crash'].iloc[-1],ZBHdur['Cumulative Return During Crash'].iloc[-1],ZTSdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],ABTaft['Cumulative Return After Crash'].iloc[-1],ABBVaft['Cumulative Return After Crash'].iloc[-1],ABMDaft['Cumulative Return After Crash'].iloc[-1],Aaft['Cumulative Return After Crash'].iloc[-1],ALXNaft['Cumulative Return After Crash'].iloc[-1],ALGNaft['Cumulative Return After Crash'].iloc[-1],ABCaft['Cumulative Return After Crash'].iloc[-1],AMGNaft['Cumulative Return After Crash'].iloc[-1],ANTMaft['Cumulative Return After Crash'].iloc[-1],BAXaft['Cumulative Return After Crash'].iloc[-1],BDXaft['Cumulative Return After Crash'].iloc[-1],BIIBaft['Cumulative Return After Crash'].iloc[-1],BSXaft['Cumulative Return After Crash'].iloc[-1],BMYaft['Cumulative Return After Crash'].iloc[-1],CAHaft['Cumulative Return After Crash'].iloc[-1],CNCaft['Cumulative Return After Crash'].iloc[-1],CERNaft['Cumulative Return After Crash'].iloc[-1],CIaft['Cumulative Return After Crash'].iloc[-1],COOaft['Cumulative Return After Crash'].iloc[-1],CVSaft['Cumulative Return After Crash'].iloc[-1],DHRaft['Cumulative Return After Crash'].iloc[-1],DVAaft['Cumulative Return After Crash'].iloc[-1],XRAYaft['Cumulative Return After Crash'].iloc[-1],DXCMaft['Cumulative Return After Crash'].iloc[-1],EWaft['Cumulative Return After Crash'].iloc[-1],GILDaft['Cumulative Return After Crash'].iloc[-1],HCAaft['Cumulative Return After Crash'].iloc[-1],HSICaft['Cumulative Return After Crash'].iloc[-1],HOLXaft['Cumulative Return After Crash'].iloc[-1],HUMaft['Cumulative Return After Crash'].iloc[-1],IDXXaft['Cumulative Return After Crash'].iloc[-1],ILMNaft['Cumulative Return After Crash'].iloc[-1],INCYaft['Cumulative Return After Crash'].iloc[-1],ISRGaft['Cumulative Return After Crash'].iloc[-1],IQVaft['Cumulative Return After Crash'].iloc[-1],JNJaft['Cumulative Return After Crash'].iloc[-1],LHaft['Cumulative Return After Crash'].iloc[-1],LLYaft['Cumulative Return After Crash'].iloc[-1],MCKaft['Cumulative Return After Crash'].iloc[-1],MDTaft['Cumulative Return After Crash'].iloc[-1],MRKaft['Cumulative Return After Crash'].iloc[-1],MTDaft['Cumulative Return After Crash'].iloc[-1],MYLaft['Cumulative Return After Crash'].iloc[-1],PKIaft['Cumulative Return After Crash'].iloc[-1],PRGOaft['Cumulative Return After Crash'].iloc[-1],PFEaft['Cumulative Return After Crash'].iloc[-1],DGXaft['Cumulative Return After Crash'].iloc[-1],REGNaft['Cumulative Return After Crash'].iloc[-1],RMDaft['Cumulative Return After Crash'].iloc[-1],STEaft['Cumulative Return After Crash'].iloc[-1],SYKaft['Cumulative Return After Crash'].iloc[-1],TFXaft['Cumulative Return After Crash'].iloc[-1],TMOaft['Cumulative Return After Crash'].iloc[-1],UNHaft['Cumulative Return After Crash'].iloc[-1],UHSaft['Cumulative Return After Crash'].iloc[-1],VARaft['Cumulative Return After Crash'].iloc[-1],VRTXaft['Cumulative Return After Crash'].iloc[-1],WATaft['Cumulative Return After Crash'].iloc[-1],WSTaft['Cumulative Return After Crash'].iloc[-1],ZBHaft['Cumulative Return After Crash'].iloc[-1],ZTSaft['Cumulative Return After Crash'].iloc[-1]]}                         

hdf2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','ABT','ABBV','ABMD','A','ALXN','ALGN','ABC','AMGN','ANTM','BAX','BDX','BIIB','BSX','BMY','CAH','CNC','CERN','CI','COO','CVS','DHR','DVA','XRAY','DXCM','EW','GILD','HCA','HSIC','HOLX','HUM','IDXX','ILMN','INCY','ISRG','IQV','JNJ','LH','LLY','MCK','MDT','MRK','MTD','MYL','PKI','PRGO','PFE','DGX','REGN','RMD','STE','SYK','TFX','TMO','UNH','UHS','VAR','VRTX','WAT','WST','ZBH','ZTS'])

pd.DataFrame(hdf2)
Out[422]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
ABT 1.054942 1.038507 0.710151 1.430436
ABBV 1.076261 1.080619 0.684495 1.455039
ABMD 1.288411 0.866883 0.787363 1.887638
A 1.116768 1.042135 0.751364 1.426227
... ... ... ... ...
VRTX 1.186593 1.094636 0.825010 1.313931
WAT 0.928784 0.966170 0.747032 1.286832
WST 1.394024 1.163233 0.751209 1.595301
ZBH 0.926063 1.067534 0.510196 1.700285
ZTS 1.177635 1.198848 0.645489 1.521800

62 rows × 4 columns

In [423]:
hdf2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[423]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
ALGN 1.037809 0.981203 0.505413 2.092724
ABMD 1.288411 0.866883 0.787363 1.887638
HUM 1.132919 1.078083 0.577994 1.818122
HOLX 1.005178 1.010740 0.557495 1.783867
IQV 1.030591 1.130908 0.511758 1.780714
... ... ... ... ...
AMGN 0.964897 0.949803 0.836995 1.213736
BDX 0.940831 0.985344 0.791367 1.206551
BIIB 1.059409 1.162752 0.799638 1.139418
MYL 0.935911 1.108581 0.752031 1.122618
GILD 1.149813 1.003745 1.083731 1.057017

62 rows × 4 columns

In [424]:
#Table of Key Statistics of Healthcare Stocks
import pandas as pd
import statistics as stats

hmeans = [ABTmean,ABBVmean,ABMDmean,Amean,ALXNmean,ALGNmean,ABCmean,AMGNmean,ANTMmean,BAXmean,BDXmean,BIIBmean,BSXmean,BMYmean,CAHmean,CNCmean,CERNmean,CImean,COOmean,CVSmean,DHRmean,DVAmean,XRAYmean,DXCMmean,EWmean,GILDmean,HCAmean,HSICmean,HOLXmean,HUMmean,IDXXmean,ILMNmean,INCYmean,ISRGmean,IQVmean,JNJmean,LHmean,LLYmean,MCKmean,MDTmean,MRKmean,MTDmean,MYLmean,PKImean,PRGOmean,PFEmean,DGXmean,REGNmean,RMDmean,STEmean,SYKmean,TFXmean,TMOmean,UNHmean,UHSmean,VARmean,VRTXmean,WATmean,WSTmean,ZBHmean,ZTSmean]
haveragemean = stats.mean(hmeans)
hvariances = [ABTvar,ABBVvar,ABMDvar,Avar,ALXNvar,ALGNvar,ABCvar,AMGNvar,ANTMvar,BAXvar,BDXvar,BIIBvar,BSXvar,BMYvar,CAHvar,CNCvar,CERNvar,CIvar,COOvar,CVSvar,DHRvar,DVAvar,XRAYvar,DXCMvar,EWvar,GILDvar,HCAvar,HSICvar,HOLXvar,HUMvar,IDXXvar,ILMNvar,INCYvar,ISRGvar,IQVvar,JNJvar,LHvar,LLYvar,MCKvar,MDTvar,MRKvar,MTDvar,MYLvar,PKIvar,PRGOvar,PFEvar,DGXvar,REGNvar,RMDvar,STEvar,SYKvar,TFXvar,TMOvar,UNHvar,UHSvar,VARvar,VRTXvar,WATvar,WSTvar,ZBHvar,ZTSvar]
haveragevar = stats.mean(hvariances)
hkurtoses = [ABTkurt,ABBVkurt,ABMDkurt,Akurt,ALXNkurt,ALGNkurt,ABCkurt,AMGNkurt,ANTMkurt,BAXkurt,BDXkurt,BIIBkurt,BSXkurt,BMYkurt,CAHkurt,CNCkurt,CERNkurt,CIkurt,COOkurt,CVSkurt,DHRkurt,DVAkurt,XRAYkurt,DXCMkurt,EWkurt,GILDkurt,HCAkurt,HSICkurt,HOLXkurt,HUMkurt,IDXXkurt,ILMNkurt,INCYkurt,ISRGkurt,IQVkurt,JNJkurt,LHkurt,LLYkurt,MCKkurt,MDTkurt,MRKkurt,MTDkurt,MYLkurt,PKIkurt,PRGOkurt,PFEkurt,DGXkurt,REGNkurt,RMDkurt,STEkurt,SYKkurt,TFXkurt,TMOkurt,UNHkurt,UHSkurt,VARkurt,VRTXkurt,WATkurt,WSTkurt,ZBHkurt,ZTSkurt]
haveragekurt = stats.mean(hkurtoses)
hskews = [ABTskew,ABBVskew,ABMDskew,Askew,ALXNskew,ALGNskew,ABCskew,AMGNskew,ANTMskew,BAXskew,BDXskew,BIIBskew,BSXskew,BMYskew,CAHskew,CNCskew,CERNskew,CIskew,COOskew,CVSskew,DHRskew,DVAskew,XRAYskew,DXCMskew,EWskew,GILDskew,HCAskew,HSICskew,HOLXskew,HUMskew,IDXXskew,ILMNskew,INCYskew,ISRGskew,IQVskew,JNJskew,LHskew,LLYskew,MCKskew,MDTskew,MRKskew,MTDskew,MYLskew,PKIskew,PRGOskew,PFEskew,DGXskew,REGNskew,RMDskew,STEskew,SYKskew,TFXskew,TMOskew,UNHskew,UHSskew,VARskew,VRTXskew,WATskew,WSTskew,ZBHskew,ZTSskew]
haverageskew = stats.mean(hskews)

Healthcare = {'Mean': [[spmean],[haveragemean],[ABTmean], [ABBVmean],[ABMDmean],[Amean],[ALXNmean],[ALGNmean],[ABCmean],[AMGNmean],[ANTMmean],[BAXmean],[BDXmean],[BIIBmean],[BSXmean],[BMYmean],[CAHmean],[CNCmean],[CERNmean],[CImean],[COOmean],[CVSmean],[DHRmean],[DVAmean],[XRAYmean],[DXCMmean],[EWmean],[GILDmean],[HCAmean],[HSICmean],[HOLXmean],[HUMmean],[IDXXmean],[ILMNmean],[INCYmean],[ISRGmean],[IQVmean],[JNJmean],[LHmean],[LLYmean],[MCKmean],[MDTmean],[MRKmean],[MTDmean],[MYLmean],[PKImean],[PRGOmean],[PFEmean],[DGXmean],[REGNmean],[RMDmean],[STEmean],[SYKmean],[TFXmean],[TMOmean],[UNHmean],[UHSmean],[VARmean],[VRTXmean],[WATmean],[WSTmean],[ZBHmean],[ZTSmean]], 
        'Variance': [[spvar],[haveragevar],[ABTvar],[ABBVvar],[ABMDvar],[Avar],[ALXNvar],[ALGNvar],[ABCvar],[AMGNvar],[ANTMvar],[BAXvar],[BDXvar],[BIIBvar],[BSXvar],[BMYvar],[CAHvar],[CNCvar],[CERNvar],[CIvar],[COOvar],[CVSvar],[DHRvar],[DVAvar],[XRAYvar],[DXCMvar],[EWvar],[GILDvar],[HCAvar],[HSICvar],[HOLXvar],[HUMvar],[IDXXvar],[ILMNvar],[INCYvar],[ISRGvar],[IQVvar],[JNJvar],[LHvar],[LLYvar],[MCKvar],[MDTvar],[MRKvar],[MTDvar],[MYLvar],[PKIvar],[PRGOvar],[PFEvar],[DGXvar],[REGNvar],[RMDvar],[STEvar],[SYKvar],[TFXvar],[TMOvar],[UNHvar],[UHSvar],[VARvar],[VRTXvar],[WATvar],[WSTvar],[ZBHvar],[ZTSvar]], 
          'Kurtosis': [[spkurt],[haveragekurt],[ABTkurt],[ABBVkurt],[ABMDkurt],[Akurt],[ALXNkurt],[ALGNkurt],[ABCkurt],[AMGNkurt],[ANTMkurt],[BAXkurt],[BDXkurt],[BIIBkurt],[BSXkurt],[BMYkurt],[CAHkurt],[CNCkurt],[CERNkurt],[CIkurt],[COOkurt],[CVSkurt],[DHRkurt],[DVAkurt],[XRAYkurt],[DXCMkurt],[EWkurt],[GILDkurt],[HCAkurt],[HSICkurt],[HOLXkurt],[HUMkurt],[IDXXkurt],[ILMNkurt],[INCYkurt],[ISRGkurt],[IQVkurt],[JNJkurt],[LHkurt],[LLYkurt],[MCKkurt],[MDTkurt],[MRKkurt],[MTDkurt],[MYLkurt],[PKIkurt],[PRGOkurt],[PFEkurt],[DGXkurt],[REGNkurt],[RMDkurt],[STEkurt],[SYKkurt],[TFXkurt],[TMOkurt],[UNHkurt],[UHSkurt],[VARkurt],[VRTXkurt],[WATkurt],[WSTkurt],[ZBHkurt],[ZTSkurt]],
          'Skew': [[spskew],[haverageskew],[ABTskew],[ABBVskew],[ABMDskew],[Askew],[ALXNskew],[ALGNskew],[ABCskew],[AMGNskew],[ANTMskew],[BAXskew],[BDXskew],[BIIBskew],[BSXskew],[BMYskew],[CAHskew],[CNCskew],[CERNskew],[CIskew],[COOskew],[CVSskew],[DHRskew],[DVAskew],[XRAYskew],[DXCMskew],[EWskew],[GILDskew],[HCAskew],[HSICskew],[HOLXskew],[HUMskew],[IDXXskew],[ILMNskew],[INCYskew],[ISRGskew],[IQVskew],[JNJskew],[LHskew],[LLYskew],[MCKskew],[MDTskew],[MRKskew],[MTDskew],[MYLskew],[PKIskew],[PRGOskew],[PFEskew],[DGXskew],[REGNskew],[RMDskew],[STEskew],[SYKskew],[TFXskew],[TMOskew],[UNHskew],[UHSskew],[VARskew],[VRTXskew],[WATskew],[WSTskew],[ZBHskew],[ZTSskew]],
        }

hdf = pd.DataFrame(Healthcare, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','ABT','ABBV','ABMD','A','ALXN','ALGN','ABC','AMGN','ANTM','BAX','BDX','BIIB','BSX','BMY','CAH','CNC','CERN','CI','COO','CVS','DHR','DVA','XRAY','DXCM','EW','GILD','HCA','HSIC','HOLX','HUM','IDXX','ILMN','INCY','ISRG','IQV','JNJ','LH','LLY','MCK','MDT','MRK','MTD','MYL','PKI','PRGO','PFE','DGX','REGN','RMD','STE','SYK','TFX','TMO','UNH','UHS','VAR','VRTX','WAT','WST','ZBH','ZTS']
)

pd.DataFrame(hdf)
Out[424]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [0.001012868830071905] [0.0011254848167398985] [3.6670562450420836] [0.07386041378796937]
ABT [0.0008776699272421629] [0.0009169295073283948] [2.673940466890218] [0.26262198628019895]
ABBV [0.0009358457134565835] [0.0007021857366763371] [5.565278154960139] [-0.5646011064008386]
ABMD [0.002564020811475632] [0.001108177172921608] [2.413552479335502] [-0.09133643538006753]
... ... ... ... ...
VRTX [0.0018393800690996041] [0.0009747974360103567] [1.453810544161585] [0.2936608271867858]
WAT [-0.0001621557031200558] [0.0008609974217150089] [2.948833805620679] [0.4948548245802351]
WST [0.003053245219432493] [0.0008417124731314435] [3.0989630205676697] [0.4906234003436329]
ZBH [8.840660516152702e-05] [0.0014196063143868416] [2.9534533376238308] [0.5044775313512859]
ZTS [0.001787640411022052] [0.0009773271907195806] [5.950525803175098] [-0.3230782560880808]

63 rows × 4 columns

In [425]:
hdf.sort_values("Mean", ascending=False)
Out[425]:
Mean Variance Kurtosis Skew
DXCM [0.004472882675905785] [0.0015690284286182757] [3.5187948474616766] [-0.25377213313842]
REGN [0.004278696180392517] [0.0009600431693299916] [1.7578774978991998] [0.12677321390539792]
WST [0.003053245219432493] [0.0008417124731314435] [3.0989630205676697] [0.4906234003436329]
ABMD [0.002564020811475632] [0.001108177172921608] [2.413552479335502] [-0.09133643538006753]
LLY [0.0022095345061726116] [0.0007943349198212294] [5.0661865383202604] [0.5375384430282122]
... ... ... ... ...
MRK [-0.0002893892583941779] [0.0006169664080701426] [2.4534148357225884] [0.12317853018006898]
CVS [-0.00030181817313611696] [0.0008725452542463455] [5.259707425699703] [-0.0487004429907901]
HCA [-0.0004602973329312929] [0.002223637692548165] [5.933374477458636] [0.5103153716504725]
XRAY [-0.0005391686399672097] [0.0011125597731663957] [2.88818385670551] [0.03522956688897247]
UHS [-0.0006741132892275317] [0.0027264874634904705] [6.983676136765076] [0.2940350257552004]

63 rows × 4 columns

Financials

In [426]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AFL = web.DataReader('AFL', 'yahoo', start, end)
AFL['returns'] = AFL['Close'].pct_change(1)
AFL['Cumulative Return'] = (1 + AFL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AFLmean = AFL['returns'].mean()
AFLvar = AFL['returns'].var()
AFLkurt = AFL['returns'].kurt()
AFLskew = AFL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AFLbef = web.DataReader('AFL', 'yahoo', start, crashstart)
AFLbef['avgreturnbefore'] = AFLbef['Close'].pct_change(1)
AFLbef['Cumulative Return Before Crash'] = (1 + AFLbef['avgreturnbefore']).cumprod()

#period of crash
AFLdur = web.DataReader('AFL', 'yahoo', crashstart, crashend)
AFLdur['avgreturnduring'] = AFLdur['Close'].pct_change(1)
AFLdur['Cumulative Return During Crash'] = (1 + AFLdur['avgreturnduring']).cumprod()

#period after crash
AFLaft = web.DataReader('AFL', 'yahoo', crashend, end)
AFLaft['avgreturnafter'] = AFLaft['Close'].pct_change(1)
AFLaft['Cumulative Return After Crash'] = (1 + AFLaft['avgreturnafter']).cumprod()
In [427]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ALL = web.DataReader('ALL', 'yahoo', start, end)
ALL['returns'] = ALL['Close'].pct_change(1)
ALL['Cumulative Return'] = (1 + ALL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ALLmean = ALL['returns'].mean()
ALLvar = ALL['returns'].var()
ALLkurt = ALL['returns'].kurt()
ALLskew = ALL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ALLbef = web.DataReader('ALL', 'yahoo', start, crashstart)
ALLbef['avgreturnbefore'] = ALLbef['Close'].pct_change(1)
ALLbef['Cumulative Return Before Crash'] = (1 + ALLbef['avgreturnbefore']).cumprod()

#period of crash
ALLdur = web.DataReader('ALL', 'yahoo', crashstart, crashend)
ALLdur['avgreturnduring'] = ALLdur['Close'].pct_change(1)
ALLdur['Cumulative Return During Crash'] = (1 + ALLdur['avgreturnduring']).cumprod()

#period after crash
ALLaft = web.DataReader('ALL', 'yahoo', crashend, end)
ALLaft['avgreturnafter'] = ALLaft['Close'].pct_change(1)
ALLaft['Cumulative Return After Crash'] = (1 + ALLaft['avgreturnafter']).cumprod()
In [428]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AXP = web.DataReader('AXP', 'yahoo', start, end)
AXP['returns'] = AXP['Close'].pct_change(1)
AXP['Cumulative Return'] = (1 + AXP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AXPmean = AXP['returns'].mean()
AXPvar = AXP['returns'].var()
AXPkurt = AXP['returns'].kurt()
AXPskew = AXP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AXPbef = web.DataReader('AXP', 'yahoo', start, crashstart)
AXPbef['avgreturnbefore'] = AXPbef['Close'].pct_change(1)
AXPbef['Cumulative Return Before Crash'] = (1 + AXPbef['avgreturnbefore']).cumprod()

#period of crash
AXPdur = web.DataReader('AXP', 'yahoo', crashstart, crashend)
AXPdur['avgreturnduring'] = AXPdur['Close'].pct_change(1)
AXPdur['Cumulative Return During Crash'] = (1 + AXPdur['avgreturnduring']).cumprod()

#period after crash
AXPaft = web.DataReader('AXP', 'yahoo', crashend, end)
AXPaft['avgreturnafter'] = AXPaft['Close'].pct_change(1)
AXPaft['Cumulative Return After Crash'] = (1 + AXPaft['avgreturnafter']).cumprod()
In [429]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AIG = web.DataReader('AIG', 'yahoo', start, end)
AIG['returns'] = AIG['Close'].pct_change(1)
AIG['Cumulative Return'] = (1 + AIG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AIGmean = AIG['returns'].mean()
AIGvar = AIG['returns'].var()
AIGkurt = AIG['returns'].kurt()
AIGskew = AIG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AIGbef = web.DataReader('AIG', 'yahoo', start, crashstart)
AIGbef['avgreturnbefore'] = AIGbef['Close'].pct_change(1)
AIGbef['Cumulative Return Before Crash'] = (1 + AIGbef['avgreturnbefore']).cumprod()

#period of crash
AIGdur = web.DataReader('AIG', 'yahoo', crashstart, crashend)
AIGdur['avgreturnduring'] = AIGdur['Close'].pct_change(1)
AIGdur['Cumulative Return During Crash'] = (1 + AIGdur['avgreturnduring']).cumprod()

#period after crash
AIGaft = web.DataReader('AIG', 'yahoo', crashend, end)
AIGaft['avgreturnafter'] = AIGaft['Close'].pct_change(1)
AIGaft['Cumulative Return After Crash'] = (1 + AIGaft['avgreturnafter']).cumprod()
In [430]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AMP = web.DataReader('AMP', 'yahoo', start, end)
AMP['returns'] = AMP['Close'].pct_change(1)
AMP['Cumulative Return'] = (1 + AMP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AMPmean = AMP['returns'].mean()
AMPvar = AMP['returns'].var()
AMPkurt = AMP['returns'].kurt()
AMPskew = AMP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AMPbef = web.DataReader('AMP', 'yahoo', start, crashstart)
AMPbef['avgreturnbefore'] = AMPbef['Close'].pct_change(1)
AMPbef['Cumulative Return Before Crash'] = (1 + AMPbef['avgreturnbefore']).cumprod()

#period of crash
AMPdur = web.DataReader('AMP', 'yahoo', crashstart, crashend)
AMPdur['avgreturnduring'] = AMPdur['Close'].pct_change(1)
AMPdur['Cumulative Return During Crash'] = (1 + AMPdur['avgreturnduring']).cumprod()

#period after crash
AMPaft = web.DataReader('AMP', 'yahoo', crashend, end)
AMPaft['avgreturnafter'] = AMPaft['Close'].pct_change(1)
AMPaft['Cumulative Return After Crash'] = (1 + AMPaft['avgreturnafter']).cumprod()
In [431]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AON = web.DataReader('AON', 'yahoo', start, end)
AON['returns'] = AON['Close'].pct_change(1)
AON['Cumulative Return'] = (1 + AON['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AONmean = AON['returns'].mean()
AONvar = AON['returns'].var()
AONkurt = AON['returns'].kurt()
AONskew = AON['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AONbef = web.DataReader('AON', 'yahoo', start, crashstart)
AONbef['avgreturnbefore'] = AONbef['Close'].pct_change(1)
AONbef['Cumulative Return Before Crash'] = (1 + AONbef['avgreturnbefore']).cumprod()

#period of crash
AONdur = web.DataReader('AON', 'yahoo', crashstart, crashend)
AONdur['avgreturnduring'] = AONdur['Close'].pct_change(1)
AONdur['Cumulative Return During Crash'] = (1 + AONdur['avgreturnduring']).cumprod()

#period after crash
AONaft = web.DataReader('AON', 'yahoo', crashend, end)
AONaft['avgreturnafter'] = AONaft['Close'].pct_change(1)
AONaft['Cumulative Return After Crash'] = (1 + AONaft['avgreturnafter']).cumprod()
In [432]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AJG = web.DataReader('AJG', 'yahoo', start, end)
AJG['returns'] = AJG['Close'].pct_change(1)
AJG['Cumulative Return'] = (1 + AJG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AJGmean = AJG['returns'].mean()
AJGvar = AJG['returns'].var()
AJGkurt = AJG['returns'].kurt()
AJGskew = AJG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AJGbef = web.DataReader('AJG', 'yahoo', start, crashstart)
AJGbef['avgreturnbefore'] = AJGbef['Close'].pct_change(1)
AJGbef['Cumulative Return Before Crash'] = (1 + AJGbef['avgreturnbefore']).cumprod()

#period of crash
AJGdur = web.DataReader('AJG', 'yahoo', crashstart, crashend)
AJGdur['avgreturnduring'] = AJGdur['Close'].pct_change(1)
AJGdur['Cumulative Return During Crash'] = (1 + AJGdur['avgreturnduring']).cumprod()

#period after crash
AJGaft = web.DataReader('AJG', 'yahoo', crashend, end)
AJGaft['avgreturnafter'] = AJGaft['Close'].pct_change(1)
AJGaft['Cumulative Return After Crash'] = (1 + AJGaft['avgreturnafter']).cumprod()
In [433]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AIZ = web.DataReader('AIZ', 'yahoo', start, end)
AIZ['returns'] = AIZ['Close'].pct_change(1)
AIZ['Cumulative Return'] = (1 + AIZ['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AIZmean = AIZ['returns'].mean()
AIZvar = AIZ['returns'].var()
AIZkurt = AIZ['returns'].kurt()
AIZskew = AIZ['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AIZbef = web.DataReader('AIZ', 'yahoo', start, crashstart)
AIZbef['avgreturnbefore'] = AIZbef['Close'].pct_change(1)
AIZbef['Cumulative Return Before Crash'] = (1 + AIZbef['avgreturnbefore']).cumprod()

#period of crash
AIZdur = web.DataReader('AIZ', 'yahoo', crashstart, crashend)
AIZdur['avgreturnduring'] = AIZdur['Close'].pct_change(1)
AIZdur['Cumulative Return During Crash'] = (1 + AIZdur['avgreturnduring']).cumprod()

#period after crash
AIZaft = web.DataReader('AIZ', 'yahoo', crashend, end)
AIZaft['avgreturnafter'] = AIZaft['Close'].pct_change(1)
AIZaft['Cumulative Return After Crash'] = (1 + AIZaft['avgreturnafter']).cumprod()
In [434]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BAC = web.DataReader('BAC', 'yahoo', start, end)
BAC['returns'] = BAC['Close'].pct_change(1)
BAC['Cumulative Return'] = (1 + BAC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BACmean = BAC['returns'].mean()
BACvar = BAC['returns'].var()
BACkurt = BAC['returns'].kurt()
BACskew = BAC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BACbef = web.DataReader('BAC', 'yahoo', start, crashstart)
BACbef['avgreturnbefore'] = BACbef['Close'].pct_change(1)
BACbef['Cumulative Return Before Crash'] = (1 + BACbef['avgreturnbefore']).cumprod()

#period of crash
BACdur = web.DataReader('BAC', 'yahoo', crashstart, crashend)
BACdur['avgreturnduring'] = BACdur['Close'].pct_change(1)
BACdur['Cumulative Return During Crash'] = (1 + BACdur['avgreturnduring']).cumprod()

#period after crash
BACaft = web.DataReader('BAC', 'yahoo', crashend, end)
BACaft['avgreturnafter'] = BACaft['Close'].pct_change(1)
BACaft['Cumulative Return After Crash'] = (1 + BACaft['avgreturnafter']).cumprod()
In [435]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BK = web.DataReader('BK', 'yahoo', start, end)
BK['returns'] = BK['Close'].pct_change(1)
BK['Cumulative Return'] = (1 + BK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BKmean = BK['returns'].mean()
BKvar = BK['returns'].var()
BKkurt = BK['returns'].kurt()
BKskew = BK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BKbef = web.DataReader('BK', 'yahoo', start, crashstart)
BKbef['avgreturnbefore'] = BKbef['Close'].pct_change(1)
BKbef['Cumulative Return Before Crash'] = (1 + BKbef['avgreturnbefore']).cumprod()

#period of crash
BKdur = web.DataReader('BK', 'yahoo', crashstart, crashend)
BKdur['avgreturnduring'] = BKdur['Close'].pct_change(1)
BKdur['Cumulative Return During Crash'] = (1 + BKdur['avgreturnduring']).cumprod()

#period after crash
BKaft = web.DataReader('BK', 'yahoo', crashend, end)
BKaft['avgreturnafter'] = BKaft['Close'].pct_change(1)
BKaft['Cumulative Return After Crash'] = (1 + BKaft['avgreturnafter']).cumprod()
In [436]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BRKB = web.DataReader('BRK-B', 'yahoo', start, end)
BRKB['returns'] = BRKB['Close'].pct_change(1)
BRKB['Cumulative Return'] = (1 + BRKB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BRKBmean = BRKB['returns'].mean()
BRKBvar = BRKB['returns'].var()
BRKBkurt = BRKB['returns'].kurt()
BRKBskew = BRKB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BRKBbef = web.DataReader('BRK-B', 'yahoo', start, crashstart)
BRKBbef['avgreturnbefore'] = BRKBbef['Close'].pct_change(1)
BRKBbef['Cumulative Return Before Crash'] = (1 + BRKBbef['avgreturnbefore']).cumprod()

#period of crash
BRKBdur = web.DataReader('BRK-B', 'yahoo', crashstart, crashend)
BRKBdur['avgreturnduring'] = BRKBdur['Close'].pct_change(1)
BRKBdur['Cumulative Return During Crash'] = (1 + BRKBdur['avgreturnduring']).cumprod()

#period after crash
BRKBaft = web.DataReader('BRK-B', 'yahoo', crashend, end)
BRKBaft['avgreturnafter'] = BRKBaft['Close'].pct_change(1)
BRKBaft['Cumulative Return After Crash'] = (1 + BRKBaft['avgreturnafter']).cumprod()
In [437]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BLK = web.DataReader('BLK', 'yahoo', start, end)
BLK['returns'] = BLK['Close'].pct_change(1)
BLK['Cumulative Return'] = (1 + BLK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BLKmean = BLK['returns'].mean()
BLKvar = BLK['returns'].var()
BLKkurt = BLK['returns'].kurt()
BLKskew = BLK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BLKbef = web.DataReader('BLK', 'yahoo', start, crashstart)
BLKbef['avgreturnbefore'] = BLKbef['Close'].pct_change(1)
BLKbef['Cumulative Return Before Crash'] = (1 + BLKbef['avgreturnbefore']).cumprod()

#period of crash
BLKdur = web.DataReader('BLK', 'yahoo', crashstart, crashend)
BLKdur['avgreturnduring'] = BLKdur['Close'].pct_change(1)
BLKdur['Cumulative Return During Crash'] = (1 + BLKdur['avgreturnduring']).cumprod()

#period after crash
BLKaft = web.DataReader('BLK', 'yahoo', crashend, end)
BLKaft['avgreturnafter'] = BLKaft['Close'].pct_change(1)
BLKaft['Cumulative Return After Crash'] = (1 + BLKaft['avgreturnafter']).cumprod()
In [438]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
COF = web.DataReader('COF', 'yahoo', start, end)
COF['returns'] = COF['Close'].pct_change(1)
COF['Cumulative Return'] = (1 + COF['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
COFmean = COF['returns'].mean()
COFvar = COF['returns'].var()
COFkurt = COF['returns'].kurt()
COFskew = COF['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
COFbef = web.DataReader('COF', 'yahoo', start, crashstart)
COFbef['avgreturnbefore'] = COFbef['Close'].pct_change(1)
COFbef['Cumulative Return Before Crash'] = (1 + COFbef['avgreturnbefore']).cumprod()

#period of crash
COFdur = web.DataReader('COF', 'yahoo', crashstart, crashend)
COFdur['avgreturnduring'] = COFdur['Close'].pct_change(1)
COFdur['Cumulative Return During Crash'] = (1 + COFdur['avgreturnduring']).cumprod()

#period after crash
COFaft = web.DataReader('COF', 'yahoo', crashend, end)
COFaft['avgreturnafter'] = COFaft['Close'].pct_change(1)
COFaft['Cumulative Return After Crash'] = (1 + COFaft['avgreturnafter']).cumprod()
In [439]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CBOE = web.DataReader('CBOE', 'yahoo', start, end)
CBOE['returns'] = CBOE['Close'].pct_change(1)
CBOE['Cumulative Return'] = (1 + CBOE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CBOEmean = CBOE['returns'].mean()
CBOEvar = CBOE['returns'].var()
CBOEkurt = CBOE['returns'].kurt()
CBOEskew = CBOE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CBOEbef = web.DataReader('CBOE', 'yahoo', start, crashstart)
CBOEbef['avgreturnbefore'] = CBOEbef['Close'].pct_change(1)
CBOEbef['Cumulative Return Before Crash'] = (1 + CBOEbef['avgreturnbefore']).cumprod()

#period of crash
CBOEdur = web.DataReader('CBOE', 'yahoo', crashstart, crashend)
CBOEdur['avgreturnduring'] = CBOEdur['Close'].pct_change(1)
CBOEdur['Cumulative Return During Crash'] = (1 + CBOEdur['avgreturnduring']).cumprod()

#period after crash
CBOEaft = web.DataReader('CBOE', 'yahoo', crashend, end)
CBOEaft['avgreturnafter'] = CBOEaft['Close'].pct_change(1)
CBOEaft['Cumulative Return After Crash'] = (1 + CBOEaft['avgreturnafter']).cumprod()
In [440]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SCHW = web.DataReader('SCHW', 'yahoo', start, end)
SCHW['returns'] = SCHW['Close'].pct_change(1)
SCHW['Cumulative Return'] = (1 + SCHW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SCHWmean = SCHW['returns'].mean()
SCHWvar = SCHW['returns'].var()
SCHWkurt = SCHW['returns'].kurt()
SCHWskew = SCHW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SCHWbef = web.DataReader('SCHW', 'yahoo', start, crashstart)
SCHWbef['avgreturnbefore'] = SCHWbef['Close'].pct_change(1)
SCHWbef['Cumulative Return Before Crash'] = (1 + SCHWbef['avgreturnbefore']).cumprod()

#period of crash
SCHWdur = web.DataReader('SCHW', 'yahoo', crashstart, crashend)
SCHWdur['avgreturnduring'] = SCHWdur['Close'].pct_change(1)
SCHWdur['Cumulative Return During Crash'] = (1 + SCHWdur['avgreturnduring']).cumprod()

#period after crash
SCHWaft = web.DataReader('SCHW', 'yahoo', crashend, end)
SCHWaft['avgreturnafter'] = SCHWaft['Close'].pct_change(1)
SCHWaft['Cumulative Return After Crash'] = (1 + SCHWaft['avgreturnafter']).cumprod()
In [441]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CB = web.DataReader('CB', 'yahoo', start, end)
CB['returns'] = CB['Close'].pct_change(1)
CB['Cumulative Return'] = (1 + CB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CBmean = CB['returns'].mean()
CBvar = CB['returns'].var()
CBkurt = CB['returns'].kurt()
CBskew = CB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CBbef = web.DataReader('CB', 'yahoo', start, crashstart)
CBbef['avgreturnbefore'] = CBbef['Close'].pct_change(1)
CBbef['Cumulative Return Before Crash'] = (1 + CBbef['avgreturnbefore']).cumprod()

#period of crash
CBdur = web.DataReader('CB', 'yahoo', crashstart, crashend)
CBdur['avgreturnduring'] = CBdur['Close'].pct_change(1)
CBdur['Cumulative Return During Crash'] = (1 + CBdur['avgreturnduring']).cumprod()

#period after crash
CBaft = web.DataReader('CB', 'yahoo', crashend, end)
CBaft['avgreturnafter'] = CBaft['Close'].pct_change(1)
CBaft['Cumulative Return After Crash'] = (1 + CBaft['avgreturnafter']).cumprod()
In [442]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CINF = web.DataReader('CINF', 'yahoo', start, end)
CINF['returns'] = CINF['Close'].pct_change(1)
CINF['Cumulative Return'] = (1 + CINF['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CINFmean = CINF['returns'].mean()
CINFvar = CINF['returns'].var()
CINFkurt = CINF['returns'].kurt()
CINFskew = CINF['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CINFbef = web.DataReader('CINF', 'yahoo', start, crashstart)
CINFbef['avgreturnbefore'] = CINFbef['Close'].pct_change(1)
CINFbef['Cumulative Return Before Crash'] = (1 + CINFbef['avgreturnbefore']).cumprod()

#period of crash
CINFdur = web.DataReader('CINF', 'yahoo', crashstart, crashend)
CINFdur['avgreturnduring'] = CINFdur['Close'].pct_change(1)
CINFdur['Cumulative Return During Crash'] = (1 + CINFdur['avgreturnduring']).cumprod()

#period after crash
CINFaft = web.DataReader('CINF', 'yahoo', crashend, end)
CINFaft['avgreturnafter'] = CINFaft['Close'].pct_change(1)
CINFaft['Cumulative Return After Crash'] = (1 + CINFaft['avgreturnafter']).cumprod()
In [443]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
C = web.DataReader('C', 'yahoo', start, end)
C['returns'] = C['Close'].pct_change(1)
C['Cumulative Return'] = (1 + C['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
Cmean = C['returns'].mean()
Cvar = C['returns'].var()
Ckurt = C['returns'].kurt()
Cskew = C['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
Cbef = web.DataReader('C', 'yahoo', start, crashstart)
Cbef['avgreturnbefore'] = Cbef['Close'].pct_change(1)
Cbef['Cumulative Return Before Crash'] = (1 + Cbef['avgreturnbefore']).cumprod()

#period of crash
Cdur = web.DataReader('C', 'yahoo', crashstart, crashend)
Cdur['avgreturnduring'] = Cdur['Close'].pct_change(1)
Cdur['Cumulative Return During Crash'] = (1 + Cdur['avgreturnduring']).cumprod()

#period after crash
Caft = web.DataReader('C', 'yahoo', crashend, end)
Caft['avgreturnafter'] = Caft['Close'].pct_change(1)
Caft['Cumulative Return After Crash'] = (1 + Caft['avgreturnafter']).cumprod()
In [444]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CFG = web.DataReader('CFG', 'yahoo', start, end)
CFG['returns'] = CFG['Close'].pct_change(1)
CFG['Cumulative Return'] = (1 + CFG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CFGmean = CFG['returns'].mean()
CFGvar = CFG['returns'].var()
CFGkurt = CFG['returns'].kurt()
CFGskew = CFG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CFGbef = web.DataReader('CFG', 'yahoo', start, crashstart)
CFGbef['avgreturnbefore'] = CFGbef['Close'].pct_change(1)
CFGbef['Cumulative Return Before Crash'] = (1 + CFGbef['avgreturnbefore']).cumprod()

#period of crash
CFGdur = web.DataReader('CFG', 'yahoo', crashstart, crashend)
CFGdur['avgreturnduring'] = CFGdur['Close'].pct_change(1)
CFGdur['Cumulative Return During Crash'] = (1 + CFGdur['avgreturnduring']).cumprod()

#period after crash
CFGaft = web.DataReader('CFG', 'yahoo', crashend, end)
CFGaft['avgreturnafter'] = CFGaft['Close'].pct_change(1)
CFGaft['Cumulative Return After Crash'] = (1 + CFGaft['avgreturnafter']).cumprod()
In [445]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CME = web.DataReader('CME', 'yahoo', start, end)
CME['returns'] = CME['Close'].pct_change(1)
CME['Cumulative Return'] = (1 + CME['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CMEmean = CME['returns'].mean()
CMEvar = CME['returns'].var()
CMEkurt = CME['returns'].kurt()
CMEskew = CME['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CMEbef = web.DataReader('CME', 'yahoo', start, crashstart)
CMEbef['avgreturnbefore'] = CMEbef['Close'].pct_change(1)
CMEbef['Cumulative Return Before Crash'] = (1 + CMEbef['avgreturnbefore']).cumprod()

#period of crash
CMEdur = web.DataReader('CME', 'yahoo', crashstart, crashend)
CMEdur['avgreturnduring'] = CMEdur['Close'].pct_change(1)
CMEdur['Cumulative Return During Crash'] = (1 + CMEdur['avgreturnduring']).cumprod()

#period after crash
CMEaft = web.DataReader('CME', 'yahoo', crashend, end)
CMEaft['avgreturnafter'] = CMEaft['Close'].pct_change(1)
CMEaft['Cumulative Return After Crash'] = (1 + CMEaft['avgreturnafter']).cumprod()
In [446]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CMA = web.DataReader('CMA', 'yahoo', start, end)
CMA['returns'] = CMA['Close'].pct_change(1)
CMA['Cumulative Return'] = (1 + CMA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CMAmean = CMA['returns'].mean()
CMAvar = CMA['returns'].var()
CMAkurt = CMA['returns'].kurt()
CMAskew = CMA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CMAbef = web.DataReader('CMA', 'yahoo', start, crashstart)
CMAbef['avgreturnbefore'] = CMAbef['Close'].pct_change(1)
CMAbef['Cumulative Return Before Crash'] = (1 + CMAbef['avgreturnbefore']).cumprod()

#period of crash
CMAdur = web.DataReader('CMA', 'yahoo', crashstart, crashend)
CMAdur['avgreturnduring'] = CMAdur['Close'].pct_change(1)
CMAdur['Cumulative Return During Crash'] = (1 + CMAdur['avgreturnduring']).cumprod()

#period after crash
CMAaft = web.DataReader('CMA', 'yahoo', crashend, end)
CMAaft['avgreturnafter'] = CMAaft['Close'].pct_change(1)
CMAaft['Cumulative Return After Crash'] = (1 + CMAaft['avgreturnafter']).cumprod()
In [447]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DFS = web.DataReader('DFS', 'yahoo', start, end)
DFS['returns'] = DFS['Close'].pct_change(1)
DFS['Cumulative Return'] = (1 + DFS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DFSmean = DFS['returns'].mean()
DFSvar = DFS['returns'].var()
DFSkurt = DFS['returns'].kurt()
DFSskew = DFS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DFSbef = web.DataReader('DFS', 'yahoo', start, crashstart)
DFSbef['avgreturnbefore'] = DFSbef['Close'].pct_change(1)
DFSbef['Cumulative Return Before Crash'] = (1 + DFSbef['avgreturnbefore']).cumprod()

#period of crash
DFSdur = web.DataReader('DFS', 'yahoo', crashstart, crashend)
DFSdur['avgreturnduring'] = DFSdur['Close'].pct_change(1)
DFSdur['Cumulative Return During Crash'] = (1 + DFSdur['avgreturnduring']).cumprod()

#period after crash
DFSaft = web.DataReader('DFS', 'yahoo', crashend, end)
DFSaft['avgreturnafter'] = DFSaft['Close'].pct_change(1)
DFSaft['Cumulative Return After Crash'] = (1 + DFSaft['avgreturnafter']).cumprod()
In [448]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ETFC = web.DataReader('ETFC', 'yahoo', start, end)
ETFC['returns'] = ETFC['Close'].pct_change(1)
ETFC['Cumulative Return'] = (1 + ETFC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ETFCmean = ETFC['returns'].mean()
ETFCvar = ETFC['returns'].var()
ETFCkurt = ETFC['returns'].kurt()
ETFCskew = ETFC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ETFCbef = web.DataReader('ETFC', 'yahoo', start, crashstart)
ETFCbef['avgreturnbefore'] = ETFCbef['Close'].pct_change(1)
ETFCbef['Cumulative Return Before Crash'] = (1 + ETFCbef['avgreturnbefore']).cumprod()

#period of crash
ETFCdur = web.DataReader('ETFC', 'yahoo', crashstart, crashend)
ETFCdur['avgreturnduring'] = ETFCdur['Close'].pct_change(1)
ETFCdur['Cumulative Return During Crash'] = (1 + ETFCdur['avgreturnduring']).cumprod()

#period after crash
ETFCaft = web.DataReader('ETFC', 'yahoo', crashend, end)
ETFCaft['avgreturnafter'] = ETFCaft['Close'].pct_change(1)
ETFCaft['Cumulative Return After Crash'] = (1 + ETFCaft['avgreturnafter']).cumprod()
In [449]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
RE = web.DataReader('RE', 'yahoo', start, end)
RE['returns'] = RE['Close'].pct_change(1)
RE['Cumulative Return'] = (1 + RE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
REmean = RE['returns'].mean()
REvar = RE['returns'].var()
REkurt = RE['returns'].kurt()
REskew = RE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
REbef = web.DataReader('RE', 'yahoo', start, crashstart)
REbef['avgreturnbefore'] = REbef['Close'].pct_change(1)
REbef['Cumulative Return Before Crash'] = (1 + REbef['avgreturnbefore']).cumprod()

#period of crash
REdur = web.DataReader('RE', 'yahoo', crashstart, crashend)
REdur['avgreturnduring'] = REdur['Close'].pct_change(1)
REdur['Cumulative Return During Crash'] = (1 + REdur['avgreturnduring']).cumprod()

#period after crash
REaft = web.DataReader('RE', 'yahoo', crashend, end)
REaft['avgreturnafter'] = REaft['Close'].pct_change(1)
REaft['Cumulative Return After Crash'] = (1 + REaft['avgreturnafter']).cumprod()
In [450]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FITB = web.DataReader('FITB', 'yahoo', start, end)
FITB['returns'] = FITB['Close'].pct_change(1)
FITB['Cumulative Return'] = (1 + FITB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FITBmean = FITB['returns'].mean()
FITBvar = FITB['returns'].var()
FITBkurt = FITB['returns'].kurt()
FITBskew = FITB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FITBbef = web.DataReader('FITB', 'yahoo', start, crashstart)
FITBbef['avgreturnbefore'] = FITBbef['Close'].pct_change(1)
FITBbef['Cumulative Return Before Crash'] = (1 + FITBbef['avgreturnbefore']).cumprod()

#period of crash
FITBdur = web.DataReader('FITB', 'yahoo', crashstart, crashend)
FITBdur['avgreturnduring'] = FITBdur['Close'].pct_change(1)
FITBdur['Cumulative Return During Crash'] = (1 + FITBdur['avgreturnduring']).cumprod()

#period after crash
FITBaft = web.DataReader('FITB', 'yahoo', crashend, end)
FITBaft['avgreturnafter'] = FITBaft['Close'].pct_change(1)
FITBaft['Cumulative Return After Crash'] = (1 + FITBaft['avgreturnafter']).cumprod()
In [451]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FRC = web.DataReader('FRC', 'yahoo', start, end)
FRC['returns'] = FRC['Close'].pct_change(1)
FRC['Cumulative Return'] = (1 + FRC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FRCmean = FRC['returns'].mean()
FRCvar = FRC['returns'].var()
FRCkurt = FRC['returns'].kurt()
FRCskew = FRC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FRCbef = web.DataReader('FRC', 'yahoo', start, crashstart)
FRCbef['avgreturnbefore'] = FRCbef['Close'].pct_change(1)
FRCbef['Cumulative Return Before Crash'] = (1 + FRCbef['avgreturnbefore']).cumprod()

#period of crash
FRCdur = web.DataReader('FRC', 'yahoo', crashstart, crashend)
FRCdur['avgreturnduring'] = FRCdur['Close'].pct_change(1)
FRCdur['Cumulative Return During Crash'] = (1 + FRCdur['avgreturnduring']).cumprod()

#period after crash
FRCaft = web.DataReader('FRC', 'yahoo', crashend, end)
FRCaft['avgreturnafter'] = FRCaft['Close'].pct_change(1)
FRCaft['Cumulative Return After Crash'] = (1 + FRCaft['avgreturnafter']).cumprod()
In [452]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BEN = web.DataReader('BEN', 'yahoo', start, end)
BEN['returns'] = BEN['Close'].pct_change(1)
BEN['Cumulative Return'] = (1 + BEN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BENmean = BEN['returns'].mean()
BENvar = BEN['returns'].var()
BENkurt = BEN['returns'].kurt()
BENskew = BEN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BENbef = web.DataReader('BEN', 'yahoo', start, crashstart)
BENbef['avgreturnbefore'] = BENbef['Close'].pct_change(1)
BENbef['Cumulative Return Before Crash'] = (1 + BENbef['avgreturnbefore']).cumprod()

#period of crash
BENdur = web.DataReader('BEN', 'yahoo', crashstart, crashend)
BENdur['avgreturnduring'] = BENdur['Close'].pct_change(1)
BENdur['Cumulative Return During Crash'] = (1 + BENdur['avgreturnduring']).cumprod()

#period after crash
BENaft = web.DataReader('BEN', 'yahoo', crashend, end)
BENaft['avgreturnafter'] = BENaft['Close'].pct_change(1)
BENaft['Cumulative Return After Crash'] = (1 + BENaft['avgreturnafter']).cumprod()
In [453]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GL = web.DataReader('GL', 'yahoo', start, end)
GL['returns'] = GL['Close'].pct_change(1)
GL['Cumulative Return'] = (1 + GL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GLmean = GL['returns'].mean()
GLvar = GL['returns'].var()
GLkurt = GL['returns'].kurt()
GLskew = GL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GLbef = web.DataReader('GL', 'yahoo', start, crashstart)
GLbef['avgreturnbefore'] = GLbef['Close'].pct_change(1)
GLbef['Cumulative Return Before Crash'] = (1 + GLbef['avgreturnbefore']).cumprod()

#period of crash
GLdur = web.DataReader('GL', 'yahoo', crashstart, crashend)
GLdur['avgreturnduring'] = GLdur['Close'].pct_change(1)
GLdur['Cumulative Return During Crash'] = (1 + GLdur['avgreturnduring']).cumprod()

#period after crash
GLaft = web.DataReader('GL', 'yahoo', crashend, end)
GLaft['avgreturnafter'] = GLaft['Close'].pct_change(1)
GLaft['Cumulative Return After Crash'] = (1 + GLaft['avgreturnafter']).cumprod()
In [454]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GS = web.DataReader('GS', 'yahoo', start, end)
GS['returns'] = GS['Close'].pct_change(1)
GS['Cumulative Return'] = (1 + GS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GSmean = GS['returns'].mean()
GSvar = GS['returns'].var()
GSkurt = GS['returns'].kurt()
GSskew = GS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GSbef = web.DataReader('GS', 'yahoo', start, crashstart)
GSbef['avgreturnbefore'] = GSbef['Close'].pct_change(1)
GSbef['Cumulative Return Before Crash'] = (1 + GSbef['avgreturnbefore']).cumprod()

#period of crash
GSdur = web.DataReader('GS', 'yahoo', crashstart, crashend)
GSdur['avgreturnduring'] = GSdur['Close'].pct_change(1)
GSdur['Cumulative Return During Crash'] = (1 + GSdur['avgreturnduring']).cumprod()

#period after crash
GSaft = web.DataReader('GS', 'yahoo', crashend, end)
GSaft['avgreturnafter'] = GSaft['Close'].pct_change(1)
GSaft['Cumulative Return After Crash'] = (1 + GSaft['avgreturnafter']).cumprod()
In [455]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HIG = web.DataReader('HIG', 'yahoo', start, end)
HIG['returns'] = HIG['Close'].pct_change(1)
HIG['Cumulative Return'] = (1 + HIG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HIGmean = HIG['returns'].mean()
HIGvar = HIG['returns'].var()
HIGkurt = HIG['returns'].kurt()
HIGskew = HIG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HIGbef = web.DataReader('HIG', 'yahoo', start, crashstart)
HIGbef['avgreturnbefore'] = HIGbef['Close'].pct_change(1)
HIGbef['Cumulative Return Before Crash'] = (1 + HIGbef['avgreturnbefore']).cumprod()

#period of crash
HIGdur = web.DataReader('HIG', 'yahoo', crashstart, crashend)
HIGdur['avgreturnduring'] = HIGdur['Close'].pct_change(1)
HIGdur['Cumulative Return During Crash'] = (1 + HIGdur['avgreturnduring']).cumprod()

#period after crash
HIGaft = web.DataReader('HIG', 'yahoo', crashend, end)
HIGaft['avgreturnafter'] = HIGaft['Close'].pct_change(1)
HIGaft['Cumulative Return After Crash'] = (1 + HIGaft['avgreturnafter']).cumprod()
In [456]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HBAN = web.DataReader('HBAN', 'yahoo', start, end)
HBAN['returns'] = HBAN['Close'].pct_change(1)
HBAN['Cumulative Return'] = (1 + HBAN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HBANmean = HBAN['returns'].mean()
HBANvar = HBAN['returns'].var()
HBANkurt = HBAN['returns'].kurt()
HBANskew = HBAN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HBANbef = web.DataReader('HBAN', 'yahoo', start, crashstart)
HBANbef['avgreturnbefore'] = HBANbef['Close'].pct_change(1)
HBANbef['Cumulative Return Before Crash'] = (1 + HBANbef['avgreturnbefore']).cumprod()

#period of crash
HBANdur = web.DataReader('HBAN', 'yahoo', crashstart, crashend)
HBANdur['avgreturnduring'] = HBANdur['Close'].pct_change(1)
HBANdur['Cumulative Return During Crash'] = (1 + HBANdur['avgreturnduring']).cumprod()

#period after crash
HBANaft = web.DataReader('HBAN', 'yahoo', crashend, end)
HBANaft['avgreturnafter'] = HBANaft['Close'].pct_change(1)
HBANaft['Cumulative Return After Crash'] = (1 + HBANaft['avgreturnafter']).cumprod()
In [457]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ICE = web.DataReader('ICE', 'yahoo', start, end)
ICE['returns'] = ICE['Close'].pct_change(1)
ICE['Cumulative Return'] = (1 + ICE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ICEmean = ICE['returns'].mean()
ICEvar = ICE['returns'].var()
ICEkurt = ICE['returns'].kurt()
ICEskew = ICE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ICEbef = web.DataReader('ICE', 'yahoo', start, crashstart)
ICEbef['avgreturnbefore'] = ICEbef['Close'].pct_change(1)
ICEbef['Cumulative Return Before Crash'] = (1 + ICEbef['avgreturnbefore']).cumprod()

#period of crash
ICEdur = web.DataReader('ICE', 'yahoo', crashstart, crashend)
ICEdur['avgreturnduring'] = ICEdur['Close'].pct_change(1)
ICEdur['Cumulative Return During Crash'] = (1 + ICEdur['avgreturnduring']).cumprod()

#period after crash
ICEaft = web.DataReader('ICE', 'yahoo', crashend, end)
ICEaft['avgreturnafter'] = ICEaft['Close'].pct_change(1)
ICEaft['Cumulative Return After Crash'] = (1 + ICEaft['avgreturnafter']).cumprod()
In [458]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IVZ = web.DataReader('IVZ', 'yahoo', start, end)
IVZ['returns'] = IVZ['Close'].pct_change(1)
IVZ['Cumulative Return'] = (1 + IVZ['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IVZmean = IVZ['returns'].mean()
IVZvar = IVZ['returns'].var()
IVZkurt = IVZ['returns'].kurt()
IVZskew = IVZ['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IVZbef = web.DataReader('IVZ', 'yahoo', start, crashstart)
IVZbef['avgreturnbefore'] = IVZbef['Close'].pct_change(1)
IVZbef['Cumulative Return Before Crash'] = (1 + IVZbef['avgreturnbefore']).cumprod()

#period of crash
IVZdur = web.DataReader('IVZ', 'yahoo', crashstart, crashend)
IVZdur['avgreturnduring'] = IVZdur['Close'].pct_change(1)
IVZdur['Cumulative Return During Crash'] = (1 + IVZdur['avgreturnduring']).cumprod()

#period after crash
IVZaft = web.DataReader('IVZ', 'yahoo', crashend, end)
IVZaft['avgreturnafter'] = IVZaft['Close'].pct_change(1)
IVZaft['Cumulative Return After Crash'] = (1 + IVZaft['avgreturnafter']).cumprod()
In [459]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
JPM = web.DataReader('JPM', 'yahoo', start, end)
JPM['returns'] = JPM['Close'].pct_change(1)
JPM['Cumulative Return'] = (1 + JPM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
JPMmean = JPM['returns'].mean()
JPMvar = JPM['returns'].var()
JPMkurt = JPM['returns'].kurt()
JPMskew = JPM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
JPMbef = web.DataReader('JPM', 'yahoo', start, crashstart)
JPMbef['avgreturnbefore'] = JPMbef['Close'].pct_change(1)
JPMbef['Cumulative Return Before Crash'] = (1 + JPMbef['avgreturnbefore']).cumprod()

#period of crash
JPMdur = web.DataReader('JPM', 'yahoo', crashstart, crashend)
JPMdur['avgreturnduring'] = JPMdur['Close'].pct_change(1)
JPMdur['Cumulative Return During Crash'] = (1 + JPMdur['avgreturnduring']).cumprod()

#period after crash
JPMaft = web.DataReader('JPM', 'yahoo', crashend, end)
JPMaft['avgreturnafter'] = JPMaft['Close'].pct_change(1)
JPMaft['Cumulative Return After Crash'] = (1 + JPMaft['avgreturnafter']).cumprod()
In [460]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KEY = web.DataReader('KEY', 'yahoo', start, end)
KEY['returns'] = KEY['Close'].pct_change(1)
KEY['Cumulative Return'] = (1 + KEY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KEYmean = KEY['returns'].mean()
KEYvar = KEY['returns'].var()
KEYkurt = KEY['returns'].kurt()
KEYskew = KEY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KEYbef = web.DataReader('KEY', 'yahoo', start, crashstart)
KEYbef['avgreturnbefore'] = KEYbef['Close'].pct_change(1)
KEYbef['Cumulative Return Before Crash'] = (1 + KEYbef['avgreturnbefore']).cumprod()

#period of crash
KEYdur = web.DataReader('KEY', 'yahoo', crashstart, crashend)
KEYdur['avgreturnduring'] = KEYdur['Close'].pct_change(1)
KEYdur['Cumulative Return During Crash'] = (1 + KEYdur['avgreturnduring']).cumprod()

#period after crash
KEYaft = web.DataReader('KEY', 'yahoo', crashend, end)
KEYaft['avgreturnafter'] = KEYaft['Close'].pct_change(1)
KEYaft['Cumulative Return After Crash'] = (1 + KEYaft['avgreturnafter']).cumprod()
In [461]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LNC = web.DataReader('LNC', 'yahoo', start, end)
LNC['returns'] = LNC['Close'].pct_change(1)
LNC['Cumulative Return'] = (1 + LNC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LNCmean = LNC['returns'].mean()
LNCvar = LNC['returns'].var()
LNCkurt = LNC['returns'].kurt()
LNCskew = LNC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LNCbef = web.DataReader('LNC', 'yahoo', start, crashstart)
LNCbef['avgreturnbefore'] = LNCbef['Close'].pct_change(1)
LNCbef['Cumulative Return Before Crash'] = (1 + LNCbef['avgreturnbefore']).cumprod()

#period of crash
LNCdur = web.DataReader('LNC', 'yahoo', crashstart, crashend)
LNCdur['avgreturnduring'] = LNCdur['Close'].pct_change(1)
LNCdur['Cumulative Return During Crash'] = (1 + LNCdur['avgreturnduring']).cumprod()

#period after crash
LNCaft = web.DataReader('LNC', 'yahoo', crashend, end)
LNCaft['avgreturnafter'] = LNCaft['Close'].pct_change(1)
LNCaft['Cumulative Return After Crash'] = (1 + LNCaft['avgreturnafter']).cumprod()
In [462]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
L = web.DataReader('L', 'yahoo', start, end)
L['returns'] = L['Close'].pct_change(1)
L['Cumulative Return'] = (1 + L['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
Lmean = L['returns'].mean()
Lvar = L['returns'].var()
Lkurt = L['returns'].kurt()
Lskew = L['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
Lbef = web.DataReader('L', 'yahoo', start, crashstart)
Lbef['avgreturnbefore'] = Lbef['Close'].pct_change(1)
Lbef['Cumulative Return Before Crash'] = (1 + Lbef['avgreturnbefore']).cumprod()

#period of crash
Ldur = web.DataReader('L', 'yahoo', crashstart, crashend)
Ldur['avgreturnduring'] = Ldur['Close'].pct_change(1)
Ldur['Cumulative Return During Crash'] = (1 + Ldur['avgreturnduring']).cumprod()

#period after crash
Laft = web.DataReader('L', 'yahoo', crashend, end)
Laft['avgreturnafter'] = Laft['Close'].pct_change(1)
Laft['Cumulative Return After Crash'] = (1 + Laft['avgreturnafter']).cumprod()
In [463]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MTB = web.DataReader('MTB', 'yahoo', start, end)
MTB['returns'] = MTB['Close'].pct_change(1)
MTB['Cumulative Return'] = (1 + MTB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MTBmean = MTB['returns'].mean()
MTBvar = MTB['returns'].var()
MTBkurt = MTB['returns'].kurt()
MTBskew = MTB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MTBbef = web.DataReader('MTB', 'yahoo', start, crashstart)
MTBbef['avgreturnbefore'] = MTBbef['Close'].pct_change(1)
MTBbef['Cumulative Return Before Crash'] = (1 + MTBbef['avgreturnbefore']).cumprod()

#period of crash
MTBdur = web.DataReader('MTB', 'yahoo', crashstart, crashend)
MTBdur['avgreturnduring'] = MTBdur['Close'].pct_change(1)
MTBdur['Cumulative Return During Crash'] = (1 + MTBdur['avgreturnduring']).cumprod()

#period after crash
MTBaft = web.DataReader('MTB', 'yahoo', crashend, end)
MTBaft['avgreturnafter'] = MTBaft['Close'].pct_change(1)
MTBaft['Cumulative Return After Crash'] = (1 + MTBaft['avgreturnafter']).cumprod()
In [464]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MKTX = web.DataReader('MKTX', 'yahoo', start, end)
MKTX['returns'] = MKTX['Close'].pct_change(1)
MKTX['Cumulative Return'] = (1 + MKTX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MKTXmean = MKTX['returns'].mean()
MKTXvar = MKTX['returns'].var()
MKTXkurt = MKTX['returns'].kurt()
MKTXskew = MKTX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MKTXbef = web.DataReader('MKTX', 'yahoo', start, crashstart)
MKTXbef['avgreturnbefore'] = MKTXbef['Close'].pct_change(1)
MKTXbef['Cumulative Return Before Crash'] = (1 + MKTXbef['avgreturnbefore']).cumprod()

#period of crash
MKTXdur = web.DataReader('MKTX', 'yahoo', crashstart, crashend)
MKTXdur['avgreturnduring'] = MKTXdur['Close'].pct_change(1)
MKTXdur['Cumulative Return During Crash'] = (1 + MKTXdur['avgreturnduring']).cumprod()

#period after crash
MKTXaft = web.DataReader('MKTX', 'yahoo', crashend, end)
MKTXaft['avgreturnafter'] = MKTXaft['Close'].pct_change(1)
MKTXaft['Cumulative Return After Crash'] = (1 + MKTXaft['avgreturnafter']).cumprod()
In [465]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MMC = web.DataReader('MMC', 'yahoo', start, end)
MMC['returns'] = MMC['Close'].pct_change(1)
MMC['Cumulative Return'] = (1 + MMC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MMCmean = MMC['returns'].mean()
MMCvar = MMC['returns'].var()
MMCkurt = MMC['returns'].kurt()
MMCskew = MMC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MMCbef = web.DataReader('MMC', 'yahoo', start, crashstart)
MMCbef['avgreturnbefore'] = MMCbef['Close'].pct_change(1)
MMCbef['Cumulative Return Before Crash'] = (1 + MMCbef['avgreturnbefore']).cumprod()

#period of crash
MMCdur = web.DataReader('MMC', 'yahoo', crashstart, crashend)
MMCdur['avgreturnduring'] = MMCdur['Close'].pct_change(1)
MMCdur['Cumulative Return During Crash'] = (1 + MMCdur['avgreturnduring']).cumprod()

#period after crash
MMCaft = web.DataReader('MMC', 'yahoo', crashend, end)
MMCaft['avgreturnafter'] = MMCaft['Close'].pct_change(1)
MMCaft['Cumulative Return After Crash'] = (1 + MMCaft['avgreturnafter']).cumprod()
In [466]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MET = web.DataReader('MET', 'yahoo', start, end)
MET['returns'] = MET['Close'].pct_change(1)
MET['Cumulative Return'] = (1 + MET['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
METmean = MET['returns'].mean()
METvar = MET['returns'].var()
METkurt = MET['returns'].kurt()
METskew = MET['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
METbef = web.DataReader('MET', 'yahoo', start, crashstart)
METbef['avgreturnbefore'] = METbef['Close'].pct_change(1)
METbef['Cumulative Return Before Crash'] = (1 + METbef['avgreturnbefore']).cumprod()

#period of crash
METdur = web.DataReader('MET', 'yahoo', crashstart, crashend)
METdur['avgreturnduring'] = METdur['Close'].pct_change(1)
METdur['Cumulative Return During Crash'] = (1 + METdur['avgreturnduring']).cumprod()

#period after crash
METaft = web.DataReader('MET', 'yahoo', crashend, end)
METaft['avgreturnafter'] = METaft['Close'].pct_change(1)
METaft['Cumulative Return After Crash'] = (1 + METaft['avgreturnafter']).cumprod()
In [467]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MCO = web.DataReader('MCO', 'yahoo', start, end)
MCO['returns'] = MCO['Close'].pct_change(1)
MCO['Cumulative Return'] = (1 + MCO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MCOmean = MCO['returns'].mean()
MCOvar = MCO['returns'].var()
MCOkurt = MCO['returns'].kurt()
MCOskew = MCO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MCObef = web.DataReader('MCO', 'yahoo', start, crashstart)
MCObef['avgreturnbefore'] = MCObef['Close'].pct_change(1)
MCObef['Cumulative Return Before Crash'] = (1 + MCObef['avgreturnbefore']).cumprod()

#period of crash
MCOdur = web.DataReader('MCO', 'yahoo', crashstart, crashend)
MCOdur['avgreturnduring'] = MCOdur['Close'].pct_change(1)
MCOdur['Cumulative Return During Crash'] = (1 + MCOdur['avgreturnduring']).cumprod()

#period after crash
MCOaft = web.DataReader('MCO', 'yahoo', crashend, end)
MCOaft['avgreturnafter'] = MCOaft['Close'].pct_change(1)
MCOaft['Cumulative Return After Crash'] = (1 + MCOaft['avgreturnafter']).cumprod()
In [468]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MS = web.DataReader('MS', 'yahoo', start, end)
MS['returns'] = MS['Close'].pct_change(1)
MS['Cumulative Return'] = (1 + MS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MSmean = MS['returns'].mean()
MSvar = MS['returns'].var()
MSkurt = MS['returns'].kurt()
MSskew = MS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MSbef = web.DataReader('MS', 'yahoo', start, crashstart)
MSbef['avgreturnbefore'] = MSbef['Close'].pct_change(1)
MSbef['Cumulative Return Before Crash'] = (1 + MSbef['avgreturnbefore']).cumprod()

#period of crash
MSdur = web.DataReader('MS', 'yahoo', crashstart, crashend)
MSdur['avgreturnduring'] = MSdur['Close'].pct_change(1)
MSdur['Cumulative Return During Crash'] = (1 + MSdur['avgreturnduring']).cumprod()

#period after crash
MSaft = web.DataReader('MS', 'yahoo', crashend, end)
MSaft['avgreturnafter'] = MSaft['Close'].pct_change(1)
MSaft['Cumulative Return After Crash'] = (1 + MSaft['avgreturnafter']).cumprod()
In [469]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MSCI = web.DataReader('MSCI', 'yahoo', start, end)
MSCI['returns'] = MSCI['Close'].pct_change(1)
MSCI['Cumulative Return'] = (1 + MSCI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MSCImean = MSCI['returns'].mean()
MSCIvar = MSCI['returns'].var()
MSCIkurt = MSCI['returns'].kurt()
MSCIskew = MSCI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MSCIbef = web.DataReader('MSCI', 'yahoo', start, crashstart)
MSCIbef['avgreturnbefore'] = MSCIbef['Close'].pct_change(1)
MSCIbef['Cumulative Return Before Crash'] = (1 + MSCIbef['avgreturnbefore']).cumprod()

#period of crash
MSCIdur = web.DataReader('MSCI', 'yahoo', crashstart, crashend)
MSCIdur['avgreturnduring'] = MSCIdur['Close'].pct_change(1)
MSCIdur['Cumulative Return During Crash'] = (1 + MSCIdur['avgreturnduring']).cumprod()

#period after crash
MSCIaft = web.DataReader('MSCI', 'yahoo', crashend, end)
MSCIaft['avgreturnafter'] = MSCIaft['Close'].pct_change(1)
MSCIaft['Cumulative Return After Crash'] = (1 + MSCIaft['avgreturnafter']).cumprod()
In [470]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NDAQ = web.DataReader('NDAQ', 'yahoo', start, end)
NDAQ['returns'] = NDAQ['Close'].pct_change(1)
NDAQ['Cumulative Return'] = (1 + NDAQ['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NDAQmean = NDAQ['returns'].mean()
NDAQvar = NDAQ['returns'].var()
NDAQkurt = NDAQ['returns'].kurt()
NDAQskew = NDAQ['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NDAQbef = web.DataReader('NDAQ', 'yahoo', start, crashstart)
NDAQbef['avgreturnbefore'] = NDAQbef['Close'].pct_change(1)
NDAQbef['Cumulative Return Before Crash'] = (1 + NDAQbef['avgreturnbefore']).cumprod()

#period of crash
NDAQdur = web.DataReader('NDAQ', 'yahoo', crashstart, crashend)
NDAQdur['avgreturnduring'] = NDAQdur['Close'].pct_change(1)
NDAQdur['Cumulative Return During Crash'] = (1 + NDAQdur['avgreturnduring']).cumprod()

#period after crash
NDAQaft = web.DataReader('NDAQ', 'yahoo', crashend, end)
NDAQaft['avgreturnafter'] = NDAQaft['Close'].pct_change(1)
NDAQaft['Cumulative Return After Crash'] = (1 + NDAQaft['avgreturnafter']).cumprod()
In [471]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NTRS = web.DataReader('NTRS', 'yahoo', start, end)
NTRS['returns'] = NTRS['Close'].pct_change(1)
NTRS['Cumulative Return'] = (1 + NTRS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NTRSmean = NTRS['returns'].mean()
NTRSvar = NTRS['returns'].var()
NTRSkurt = NTRS['returns'].kurt()
NTRSskew = NTRS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NTRSbef = web.DataReader('NTRS', 'yahoo', start, crashstart)
NTRSbef['avgreturnbefore'] = NTRSbef['Close'].pct_change(1)
NTRSbef['Cumulative Return Before Crash'] = (1 + NTRSbef['avgreturnbefore']).cumprod()

#period of crash
NTRSdur = web.DataReader('NTRS', 'yahoo', crashstart, crashend)
NTRSdur['avgreturnduring'] = NTRSdur['Close'].pct_change(1)
NTRSdur['Cumulative Return During Crash'] = (1 + NTRSdur['avgreturnduring']).cumprod()

#period after crash
NTRSaft = web.DataReader('NTRS', 'yahoo', crashend, end)
NTRSaft['avgreturnafter'] = NTRSaft['Close'].pct_change(1)
NTRSaft['Cumulative Return After Crash'] = (1 + NTRSaft['avgreturnafter']).cumprod()
In [472]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PBCT = web.DataReader('PBCT', 'yahoo', start, end)
PBCT['returns'] = PBCT['Close'].pct_change(1)
PBCT['Cumulative Return'] = (1 + PBCT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PBCTmean = PBCT['returns'].mean()
PBCTvar = PBCT['returns'].var()
PBCTkurt = PBCT['returns'].kurt()
PBCTskew = PBCT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PBCTbef = web.DataReader('PBCT', 'yahoo', start, crashstart)
PBCTbef['avgreturnbefore'] = PBCTbef['Close'].pct_change(1)
PBCTbef['Cumulative Return Before Crash'] = (1 + PBCTbef['avgreturnbefore']).cumprod()

#period of crash
PBCTdur = web.DataReader('PBCT', 'yahoo', crashstart, crashend)
PBCTdur['avgreturnduring'] = PBCTdur['Close'].pct_change(1)
PBCTdur['Cumulative Return During Crash'] = (1 + PBCTdur['avgreturnduring']).cumprod()

#period after crash
PBCTaft = web.DataReader('PBCT', 'yahoo', crashend, end)
PBCTaft['avgreturnafter'] = PBCTaft['Close'].pct_change(1)
PBCTaft['Cumulative Return After Crash'] = (1 + PBCTaft['avgreturnafter']).cumprod()
In [473]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PNC = web.DataReader('PNC', 'yahoo', start, end)
PNC['returns'] = PNC['Close'].pct_change(1)
PNC['Cumulative Return'] = (1 + PNC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PNCmean = PNC['returns'].mean()
PNCvar = PNC['returns'].var()
PNCkurt = PNC['returns'].kurt()
PNCskew = PNC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PNCbef = web.DataReader('PNC', 'yahoo', start, crashstart)
PNCbef['avgreturnbefore'] = PNCbef['Close'].pct_change(1)
PNCbef['Cumulative Return Before Crash'] = (1 + PNCbef['avgreturnbefore']).cumprod()

#period of crash
PNCdur = web.DataReader('PNC', 'yahoo', crashstart, crashend)
PNCdur['avgreturnduring'] = PNCdur['Close'].pct_change(1)
PNCdur['Cumulative Return During Crash'] = (1 + PNCdur['avgreturnduring']).cumprod()

#period after crash
PNCaft = web.DataReader('PNC', 'yahoo', crashend, end)
PNCaft['avgreturnafter'] = PNCaft['Close'].pct_change(1)
PNCaft['Cumulative Return After Crash'] = (1 + PNCaft['avgreturnafter']).cumprod()
In [474]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PFG = web.DataReader('PFG', 'yahoo', start, end)
PFG['returns'] = PFG['Close'].pct_change(1)
PFG['Cumulative Return'] = (1 + PFG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PFGmean = PFG['returns'].mean()
PFGvar = PFG['returns'].var()
PFGkurt = PFG['returns'].kurt()
PFGskew = PFG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PFGbef = web.DataReader('PFG', 'yahoo', start, crashstart)
PFGbef['avgreturnbefore'] = PFGbef['Close'].pct_change(1)
PFGbef['Cumulative Return Before Crash'] = (1 + PFGbef['avgreturnbefore']).cumprod()

#period of crash
PFGdur = web.DataReader('PFG', 'yahoo', crashstart, crashend)
PFGdur['avgreturnduring'] = PFGdur['Close'].pct_change(1)
PFGdur['Cumulative Return During Crash'] = (1 + PFGdur['avgreturnduring']).cumprod()

#period after crash
PFGaft = web.DataReader('PFG', 'yahoo', crashend, end)
PFGaft['avgreturnafter'] = PFGaft['Close'].pct_change(1)
PFGaft['Cumulative Return After Crash'] = (1 + PFGaft['avgreturnafter']).cumprod()
In [475]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PGR = web.DataReader('PGR', 'yahoo', start, end)
PGR['returns'] = PGR['Close'].pct_change(1)
PGR['Cumulative Return'] = (1 + PGR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PGRmean = PGR['returns'].mean()
PGRvar = PGR['returns'].var()
PGRkurt = PGR['returns'].kurt()
PGRskew = PGR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PGRbef = web.DataReader('PGR', 'yahoo', start, crashstart)
PGRbef['avgreturnbefore'] = PGRbef['Close'].pct_change(1)
PGRbef['Cumulative Return Before Crash'] = (1 + PGRbef['avgreturnbefore']).cumprod()

#period of crash
PGRdur = web.DataReader('PGR', 'yahoo', crashstart, crashend)
PGRdur['avgreturnduring'] = PGRdur['Close'].pct_change(1)
PGRdur['Cumulative Return During Crash'] = (1 + PGRdur['avgreturnduring']).cumprod()

#period after crash
PGRaft = web.DataReader('PGR', 'yahoo', crashend, end)
PGRaft['avgreturnafter'] = PGRaft['Close'].pct_change(1)
PGRaft['Cumulative Return After Crash'] = (1 + PGRaft['avgreturnafter']).cumprod()
In [476]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PRU = web.DataReader('PRU', 'yahoo', start, end)
PRU['returns'] = PRU['Close'].pct_change(1)
PRU['Cumulative Return'] = (1 + PRU['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PRUmean = PRU['returns'].mean()
PRUvar = PRU['returns'].var()
PRUkurt = PRU['returns'].kurt()
PRUskew = PRU['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PRUbef = web.DataReader('PRU', 'yahoo', start, crashstart)
PRUbef['avgreturnbefore'] = PRUbef['Close'].pct_change(1)
PRUbef['Cumulative Return Before Crash'] = (1 + PRUbef['avgreturnbefore']).cumprod()

#period of crash
PRUdur = web.DataReader('PRU', 'yahoo', crashstart, crashend)
PRUdur['avgreturnduring'] = PRUdur['Close'].pct_change(1)
PRUdur['Cumulative Return During Crash'] = (1 + PRUdur['avgreturnduring']).cumprod()

#period after crash
PRUaft = web.DataReader('PRU', 'yahoo', crashend, end)
PRUaft['avgreturnafter'] = PRUaft['Close'].pct_change(1)
PRUaft['Cumulative Return After Crash'] = (1 + PRUaft['avgreturnafter']).cumprod()
In [477]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
RJF = web.DataReader('RJF', 'yahoo', start, end)
RJF['returns'] = RJF['Close'].pct_change(1)
RJF['Cumulative Return'] = (1 + RJF['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
RJFmean = RJF['returns'].mean()
RJFvar = RJF['returns'].var()
RJFkurt = RJF['returns'].kurt()
RJFskew = RJF['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
RJFbef = web.DataReader('RJF', 'yahoo', start, crashstart)
RJFbef['avgreturnbefore'] = RJFbef['Close'].pct_change(1)
RJFbef['Cumulative Return Before Crash'] = (1 + RJFbef['avgreturnbefore']).cumprod()

#period of crash
RJFdur = web.DataReader('RJF', 'yahoo', crashstart, crashend)
RJFdur['avgreturnduring'] = RJFdur['Close'].pct_change(1)
RJFdur['Cumulative Return During Crash'] = (1 + RJFdur['avgreturnduring']).cumprod()

#period after crash
RJFaft = web.DataReader('RJF', 'yahoo', crashend, end)
RJFaft['avgreturnafter'] = RJFaft['Close'].pct_change(1)
RJFaft['Cumulative Return After Crash'] = (1 + RJFaft['avgreturnafter']).cumprod()
In [478]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
RF = web.DataReader('RF', 'yahoo', start, end)
RF['returns'] = RF['Close'].pct_change(1)
RF['Cumulative Return'] = (1 + RF['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
RFmean = RF['returns'].mean()
RFvar = RF['returns'].var()
RFkurt = RF['returns'].kurt()
RFskew = RF['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
RFbef = web.DataReader('RF', 'yahoo', start, crashstart)
RFbef['avgreturnbefore'] = RFbef['Close'].pct_change(1)
RFbef['Cumulative Return Before Crash'] = (1 + RFbef['avgreturnbefore']).cumprod()

#period of crash
RFdur = web.DataReader('RF', 'yahoo', crashstart, crashend)
RFdur['avgreturnduring'] = RFdur['Close'].pct_change(1)
RFdur['Cumulative Return During Crash'] = (1 + RFdur['avgreturnduring']).cumprod()

#period after crash
RFaft = web.DataReader('RF', 'yahoo', crashend, end)
RFaft['avgreturnafter'] = RFaft['Close'].pct_change(1)
RFaft['Cumulative Return After Crash'] = (1 + RFaft['avgreturnafter']).cumprod()
In [479]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SPGI = web.DataReader('SPGI', 'yahoo', start, end)
SPGI['returns'] = SPGI['Close'].pct_change(1)
SPGI['Cumulative Return'] = (1 + SPGI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SPGImean = SPGI['returns'].mean()
SPGIvar = SPGI['returns'].var()
SPGIkurt = SPGI['returns'].kurt()
SPGIskew = SPGI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SPGIbef = web.DataReader('SPGI', 'yahoo', start, crashstart)
SPGIbef['avgreturnbefore'] = SPGIbef['Close'].pct_change(1)
SPGIbef['Cumulative Return Before Crash'] = (1 + SPGIbef['avgreturnbefore']).cumprod()

#period of crash
SPGIdur = web.DataReader('SPGI', 'yahoo', crashstart, crashend)
SPGIdur['avgreturnduring'] = SPGIdur['Close'].pct_change(1)
SPGIdur['Cumulative Return During Crash'] = (1 + SPGIdur['avgreturnduring']).cumprod()

#period after crash
SPGIaft = web.DataReader('SPGI', 'yahoo', crashend, end)
SPGIaft['avgreturnafter'] = SPGIaft['Close'].pct_change(1)
SPGIaft['Cumulative Return After Crash'] = (1 + SPGIaft['avgreturnafter']).cumprod()
In [480]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
STT = web.DataReader('STT', 'yahoo', start, end)
STT['returns'] = STT['Close'].pct_change(1)
STT['Cumulative Return'] = (1 + STT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
STTmean = STT['returns'].mean()
STTvar = STT['returns'].var()
STTkurt = STT['returns'].kurt()
STTskew = STT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
STTbef = web.DataReader('STT', 'yahoo', start, crashstart)
STTbef['avgreturnbefore'] = STTbef['Close'].pct_change(1)
STTbef['Cumulative Return Before Crash'] = (1 + STTbef['avgreturnbefore']).cumprod()

#period of crash
STTdur = web.DataReader('STT', 'yahoo', crashstart, crashend)
STTdur['avgreturnduring'] = STTdur['Close'].pct_change(1)
STTdur['Cumulative Return During Crash'] = (1 + STTdur['avgreturnduring']).cumprod()

#period after crash
STTaft = web.DataReader('STT', 'yahoo', crashend, end)
STTaft['avgreturnafter'] = STTaft['Close'].pct_change(1)
STTaft['Cumulative Return After Crash'] = (1 + STTaft['avgreturnafter']).cumprod()
In [481]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SIVB = web.DataReader('SIVB', 'yahoo', start, end)
SIVB['returns'] = SIVB['Close'].pct_change(1)
SIVB['Cumulative Return'] = (1 + SIVB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SIVBmean = SIVB['returns'].mean()
SIVBvar = SIVB['returns'].var()
SIVBkurt = SIVB['returns'].kurt()
SIVBskew = SIVB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SIVBbef = web.DataReader('SIVB', 'yahoo', start, crashstart)
SIVBbef['avgreturnbefore'] = SIVBbef['Close'].pct_change(1)
SIVBbef['Cumulative Return Before Crash'] = (1 + SIVBbef['avgreturnbefore']).cumprod()

#period of crash
SIVBdur = web.DataReader('SIVB', 'yahoo', crashstart, crashend)
SIVBdur['avgreturnduring'] = SIVBdur['Close'].pct_change(1)
SIVBdur['Cumulative Return During Crash'] = (1 + SIVBdur['avgreturnduring']).cumprod()

#period after crash
SIVBaft = web.DataReader('SIVB', 'yahoo', crashend, end)
SIVBaft['avgreturnafter'] = SIVBaft['Close'].pct_change(1)
SIVBaft['Cumulative Return After Crash'] = (1 + SIVBaft['avgreturnafter']).cumprod()
In [482]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SYF = web.DataReader('SYF', 'yahoo', start, end)
SYF['returns'] = SYF['Close'].pct_change(1)
SYF['Cumulative Return'] = (1 + SYF['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SYFmean = SYF['returns'].mean()
SYFvar = SYF['returns'].var()
SYFkurt = SYF['returns'].kurt()
SYFskew = SYF['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SYFbef = web.DataReader('SYF', 'yahoo', start, crashstart)
SYFbef['avgreturnbefore'] = SYFbef['Close'].pct_change(1)
SYFbef['Cumulative Return Before Crash'] = (1 + SYFbef['avgreturnbefore']).cumprod()

#period of crash
SYFdur = web.DataReader('SYF', 'yahoo', crashstart, crashend)
SYFdur['avgreturnduring'] = SYFdur['Close'].pct_change(1)
SYFdur['Cumulative Return During Crash'] = (1 + SYFdur['avgreturnduring']).cumprod()

#period after crash
SYFaft = web.DataReader('SYF', 'yahoo', crashend, end)
SYFaft['avgreturnafter'] = SYFaft['Close'].pct_change(1)
SYFaft['Cumulative Return After Crash'] = (1 + SYFaft['avgreturnafter']).cumprod()
In [483]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TROW = web.DataReader('TROW', 'yahoo', start, end)
TROW['returns'] = TROW['Close'].pct_change(1)
TROW['Cumulative Return'] = (1 + TROW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TROWmean = TROW['returns'].mean()
TROWvar = TROW['returns'].var()
TROWkurt = TROW['returns'].kurt()
TROWskew = TROW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TROWbef = web.DataReader('TROW', 'yahoo', start, crashstart)
TROWbef['avgreturnbefore'] = TROWbef['Close'].pct_change(1)
TROWbef['Cumulative Return Before Crash'] = (1 + TROWbef['avgreturnbefore']).cumprod()

#period of crash
TROWdur = web.DataReader('TROW', 'yahoo', crashstart, crashend)
TROWdur['avgreturnduring'] = TROWdur['Close'].pct_change(1)
TROWdur['Cumulative Return During Crash'] = (1 + TROWdur['avgreturnduring']).cumprod()

#period after crash
TROWaft = web.DataReader('TROW', 'yahoo', crashend, end)
TROWaft['avgreturnafter'] = TROWaft['Close'].pct_change(1)
TROWaft['Cumulative Return After Crash'] = (1 + TROWaft['avgreturnafter']).cumprod()
In [484]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TRV = web.DataReader('TRV', 'yahoo', start, end)
TRV['returns'] = TRV['Close'].pct_change(1)
TRV['Cumulative Return'] = (1 + TRV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TRVmean = TRV['returns'].mean()
TRVvar = TRV['returns'].var()
TRVkurt = TRV['returns'].kurt()
TRVskew = TRV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TRVbef = web.DataReader('TRV', 'yahoo', start, crashstart)
TRVbef['avgreturnbefore'] = TRVbef['Close'].pct_change(1)
TRVbef['Cumulative Return Before Crash'] = (1 + TRVbef['avgreturnbefore']).cumprod()

#period of crash
TRVdur = web.DataReader('TRV', 'yahoo', crashstart, crashend)
TRVdur['avgreturnduring'] = TRVdur['Close'].pct_change(1)
TRVdur['Cumulative Return During Crash'] = (1 + TRVdur['avgreturnduring']).cumprod()

#period after crash
TRVaft = web.DataReader('TRV', 'yahoo', crashend, end)
TRVaft['avgreturnafter'] = TRVaft['Close'].pct_change(1)
TRVaft['Cumulative Return After Crash'] = (1 + TRVaft['avgreturnafter']).cumprod()
In [485]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TFC = web.DataReader('TFC', 'yahoo', start, end)
TFC['returns'] = TFC['Close'].pct_change(1)
TFC['Cumulative Return'] = (1 + TFC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TFCmean = TFC['returns'].mean()
TFCvar = TFC['returns'].var()
TFCkurt = TFC['returns'].kurt()
TFCskew = TFC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TFCbef = web.DataReader('TFC', 'yahoo', start, crashstart)
TFCbef['avgreturnbefore'] = TFCbef['Close'].pct_change(1)
TFCbef['Cumulative Return Before Crash'] = (1 + TFCbef['avgreturnbefore']).cumprod()

#period of crash
TFCdur = web.DataReader('TFC', 'yahoo', crashstart, crashend)
TFCdur['avgreturnduring'] = TFCdur['Close'].pct_change(1)
TFCdur['Cumulative Return During Crash'] = (1 + TFCdur['avgreturnduring']).cumprod()

#period after crash
TFCaft = web.DataReader('TFC', 'yahoo', crashend, end)
TFCaft['avgreturnafter'] = TFCaft['Close'].pct_change(1)
TFCaft['Cumulative Return After Crash'] = (1 + TFCaft['avgreturnafter']).cumprod()
In [486]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
USB = web.DataReader('USB', 'yahoo', start, end)
USB['returns'] = USB['Close'].pct_change(1)
USB['Cumulative Return'] = (1 + USB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
USBmean = USB['returns'].mean()
USBvar = USB['returns'].var()
USBkurt = USB['returns'].kurt()
USBskew = USB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
USBbef = web.DataReader('USB', 'yahoo', start, crashstart)
USBbef['avgreturnbefore'] = USBbef['Close'].pct_change(1)
USBbef['Cumulative Return Before Crash'] = (1 + USBbef['avgreturnbefore']).cumprod()

#period of crash
USBdur = web.DataReader('USB', 'yahoo', crashstart, crashend)
USBdur['avgreturnduring'] = USBdur['Close'].pct_change(1)
USBdur['Cumulative Return During Crash'] = (1 + USBdur['avgreturnduring']).cumprod()

#period after crash
USBaft = web.DataReader('USB', 'yahoo', crashend, end)
USBaft['avgreturnafter'] = USBaft['Close'].pct_change(1)
USBaft['Cumulative Return After Crash'] = (1 + USBaft['avgreturnafter']).cumprod()
In [487]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
UNM = web.DataReader('UNM', 'yahoo', start, end)
UNM['returns'] = UNM['Close'].pct_change(1)
UNM['Cumulative Return'] = (1 + UNM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
UNMmean = UNM['returns'].mean()
UNMvar = UNM['returns'].var()
UNMkurt = UNM['returns'].kurt()
UNMskew = UNM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
UNMbef = web.DataReader('UNM', 'yahoo', start, crashstart)
UNMbef['avgreturnbefore'] = UNMbef['Close'].pct_change(1)
UNMbef['Cumulative Return Before Crash'] = (1 + UNMbef['avgreturnbefore']).cumprod()

#period of crash
UNMdur = web.DataReader('UNM', 'yahoo', crashstart, crashend)
UNMdur['avgreturnduring'] = UNMdur['Close'].pct_change(1)
UNMdur['Cumulative Return During Crash'] = (1 + UNMdur['avgreturnduring']).cumprod()

#period after crash
UNMaft = web.DataReader('UNM', 'yahoo', crashend, end)
UNMaft['avgreturnafter'] = UNMaft['Close'].pct_change(1)
UNMaft['Cumulative Return After Crash'] = (1 + UNMaft['avgreturnafter']).cumprod()
In [488]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WRB = web.DataReader('WRB', 'yahoo', start, end)
WRB['returns'] = WRB['Close'].pct_change(1)
WRB['Cumulative Return'] = (1 + WRB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WRBmean = WRB['returns'].mean()
WRBvar = WRB['returns'].var()
WRBkurt = WRB['returns'].kurt()
WRBskew = WRB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WRBbef = web.DataReader('WRB', 'yahoo', start, crashstart)
WRBbef['avgreturnbefore'] = WRBbef['Close'].pct_change(1)
WRBbef['Cumulative Return Before Crash'] = (1 + WRBbef['avgreturnbefore']).cumprod()

#period of crash
WRBdur = web.DataReader('WRB', 'yahoo', crashstart, crashend)
WRBdur['avgreturnduring'] = WRBdur['Close'].pct_change(1)
WRBdur['Cumulative Return During Crash'] = (1 + WRBdur['avgreturnduring']).cumprod()

#period after crash
WRBaft = web.DataReader('WRB', 'yahoo', crashend, end)
WRBaft['avgreturnafter'] = WRBaft['Close'].pct_change(1)
WRBaft['Cumulative Return After Crash'] = (1 + WRBaft['avgreturnafter']).cumprod()
In [489]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WFC = web.DataReader('WFC', 'yahoo', start, end)
WFC['returns'] = WFC['Close'].pct_change(1)
WFC['Cumulative Return'] = (1 + WFC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WFCmean = WFC['returns'].mean()
WFCvar = WFC['returns'].var()
WFCkurt = WFC['returns'].kurt()
WFCskew = WFC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WFCbef = web.DataReader('WFC', 'yahoo', start, crashstart)
WFCbef['avgreturnbefore'] = WFCbef['Close'].pct_change(1)
WFCbef['Cumulative Return Before Crash'] = (1 + WFCbef['avgreturnbefore']).cumprod()

#period of crash
WFCdur = web.DataReader('WFC', 'yahoo', crashstart, crashend)
WFCdur['avgreturnduring'] = WFCdur['Close'].pct_change(1)
WFCdur['Cumulative Return During Crash'] = (1 + WFCdur['avgreturnduring']).cumprod()

#period after crash
WFCaft = web.DataReader('WFC', 'yahoo', crashend, end)
WFCaft['avgreturnafter'] = WFCaft['Close'].pct_change(1)
WFCaft['Cumulative Return After Crash'] = (1 + WFCaft['avgreturnafter']).cumprod()
In [490]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WLTW = web.DataReader('WLTW', 'yahoo', start, end)
WLTW['returns'] = WLTW['Close'].pct_change(1)
WLTW['Cumulative Return'] = (1 + WLTW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WLTWmean = WLTW['returns'].mean()
WLTWvar = WLTW['returns'].var()
WLTWkurt = WLTW['returns'].kurt()
WLTWskew = WLTW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WLTWbef = web.DataReader('WLTW', 'yahoo', start, crashstart)
WLTWbef['avgreturnbefore'] = WLTWbef['Close'].pct_change(1)
WLTWbef['Cumulative Return Before Crash'] = (1 + WLTWbef['avgreturnbefore']).cumprod()

#period of crash
WLTWdur = web.DataReader('WLTW', 'yahoo', crashstart, crashend)
WLTWdur['avgreturnduring'] = WLTWdur['Close'].pct_change(1)
WLTWdur['Cumulative Return During Crash'] = (1 + WLTWdur['avgreturnduring']).cumprod()

#period after crash
WLTWaft = web.DataReader('WLTW', 'yahoo', crashend, end)
WLTWaft['avgreturnafter'] = WLTWaft['Close'].pct_change(1)
WLTWaft['Cumulative Return After Crash'] = (1 + WLTWaft['avgreturnafter']).cumprod()
In [491]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ZION = web.DataReader('ZION', 'yahoo', start, end)
ZION['returns'] = ZION['Close'].pct_change(1)
ZION['Cumulative Return'] = (1 + ZION['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ZIONmean = ZION['returns'].mean()
ZIONvar = ZION['returns'].var()
ZIONkurt = ZION['returns'].kurt()
ZIONskew = ZION['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ZIONbef = web.DataReader('ZION', 'yahoo', start, crashstart)
ZIONbef['avgreturnbefore'] = ZIONbef['Close'].pct_change(1)
ZIONbef['Cumulative Return Before Crash'] = (1 + ZIONbef['avgreturnbefore']).cumprod()

#period of crash
ZIONdur = web.DataReader('ZION', 'yahoo', crashstart, crashend)
ZIONdur['avgreturnduring'] = ZIONdur['Close'].pct_change(1)
ZIONdur['Cumulative Return During Crash'] = (1 + ZIONdur['avgreturnduring']).cumprod()

#period after crash
ZIONaft = web.DataReader('ZION', 'yahoo', crashend, end)
ZIONaft['avgreturnafter'] = ZIONaft['Close'].pct_change(1)
ZIONaft['Cumulative Return After Crash'] = (1 + ZIONaft['avgreturnafter']).cumprod()
In [492]:
#KDE Plot for Financials Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
AFL['returns'].plot(kind = 'kde', label ='AFL', figsize = (10,8), alpha=0.5, title = 'KDE of Financials Stocks')
ALL['returns'].plot(kind = 'kde', label ='ALL', alpha=0.5)
AXP['returns'].plot(kind = 'kde', label ='AXP', alpha=0.5)
AIG['returns'].plot(kind = 'kde', label ='AIG', alpha=0.5)
AMP['returns'].plot(kind = 'kde', label ='AMP', alpha=0.5)
AON['returns'].plot(kind = 'kde', label ='AON', alpha=0.5)
AJG['returns'].plot(kind = 'kde', label ='AJG', alpha=0.5)
AIZ['returns'].plot(kind = 'kde', label ='AIZ', alpha=0.5)
BAC['returns'].plot(kind = 'kde', label ='BAC', alpha=0.5)
BK['returns'].plot(kind = 'kde', label ='BK', alpha=0.5)
BRKB['returns'].plot(kind = 'kde', label ='BRK.B', alpha=0.5)
BLK['returns'].plot(kind = 'kde', label ='BLK', alpha=0.5)
COF['returns'].plot(kind = 'kde', label ='COF', alpha=0.5)
CBOE['returns'].plot(kind = 'kde', label ='CBOE', alpha=0.5)
SCHW['returns'].plot(kind = 'kde', label = 'SCHW', alpha=0.5)
CB['returns'].plot(kind = 'kde', label ='CB', alpha=0.5)
CINF['returns'].plot(kind = 'kde', label ='CINF', alpha=0.5)
C['returns'].plot(kind = 'kde', label ='C', alpha=0.5)
CFG['returns'].plot(kind = 'kde', label ='CFG', alpha=0.5)
CME['returns'].plot(kind = 'kde', label ='CME', alpha=0.5)
CMA['returns'].plot(kind = 'kde', label ='CMA', alpha=0.5)
DFS['returns'].plot(kind = 'kde', label ='DFS', alpha=0.5)
ETFC['returns'].plot(kind = 'kde', label ='ETFC', alpha=0.5)
RE['returns'].plot(kind = 'kde', label ='RE', alpha=0.5)
FITB['returns'].plot(kind = 'kde', label ='FITB', alpha=0.5)
FRC['returns'].plot(kind = 'kde', label ='FRC', alpha=0.5)
BEN['returns'].plot(kind = 'kde', label ='BEN', alpha=0.5)
GL['returns'].plot(kind = 'kde', label ='GL', alpha=0.5)
GS['returns'].plot(kind = 'kde', label ='GS', alpha=0.5)
HIG['returns'].plot(kind = 'kde', label ='HIG', alpha=0.5)
HBAN['returns'].plot(kind = 'kde', label ='HBAN', alpha=0.5)
ICE['returns'].plot(kind = 'kde', label ='ICE', alpha=0.5)
IVZ['returns'].plot(kind = 'kde', label ='IVZ', alpha=0.5)
JPM['returns'].plot(kind = 'kde', label ='JPM', alpha=0.5)
KEY['returns'].plot(kind = 'kde', label ='KEY', alpha=0.5)
LNC['returns'].plot(kind = 'kde', label ='LNC', alpha=0.5)
L['returns'].plot(kind = 'kde', label ='L', alpha=0.5)
MTB['returns'].plot(kind = 'kde', label ='MTB', alpha=0.5)
MKTX['returns'].plot(kind = 'kde', label ='MKTX', alpha=0.5)
MMC['returns'].plot(kind = 'kde', label ='MMC', alpha=0.5)
MET['returns'].plot(kind = 'kde', label ='MET', alpha=0.5)
MCO['returns'].plot(kind = 'kde', label ='MCO', alpha=0.5)
MS['returns'].plot(kind = 'kde', label ='MS', alpha=0.5)
MSCI['returns'].plot(kind = 'kde', label ='MSCI', alpha=0.5)
NDAQ['returns'].plot(kind = 'kde', label ='NDAQ', alpha=0.5)
NTRS['returns'].plot(kind = 'kde', label ='NTRS', alpha=0.5)
PBCT['returns'].plot(kind = 'kde', label ='PBCT', alpha=0.5)
PNC['returns'].plot(kind = 'kde', label ='PNC', alpha=0.5)
PFG['returns'].plot(kind = 'kde', label ='PFG', alpha=0.5)
PGR['returns'].plot(kind = 'kde', label ='PGR', alpha=0.5)
PRU['returns'].plot(kind = 'kde', label ='PRU', alpha=0.5)
RJF['returns'].plot(kind = 'kde', label ='RJF', alpha=0.5)
RF['returns'].plot(kind = 'kde', label ='RF', alpha=0.5)
SPGI['returns'].plot(kind = 'kde', label ='SPGI', alpha=0.5)
STT['returns'].plot(kind = 'kde', label ='STT', alpha=0.5)
SIVB['returns'].plot(kind = 'kde', label ='SIVB', alpha=0.5)
SYF['returns'].plot(kind = 'kde', label ='SYF', alpha=0.5)
TROW['returns'].plot(kind = 'kde', label ='TROW', alpha=0.5)
TRV['returns'].plot(kind = 'kde', label ='TRV', alpha=0.5)
TFC['returns'].plot(kind = 'kde', label ='TFC', alpha=0.5)
USB['returns'].plot(kind = 'kde', label ='USB', alpha=0.5)
UNM['returns'].plot(kind = 'kde', label ='UNM', alpha=0.5)
WRB['returns'].plot(kind = 'kde', label ='WRB', alpha=0.5)
WFC['returns'].plot(kind = 'kde', label ='WFC', alpha=0.5)
WLTW['returns'].plot(kind = 'kde', label ='WLTW', alpha=0.5)
ZION['returns'].plot(kind = 'kde', label ='ZION', alpha=0.5)

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [493]:
#Boxplots comparing returns for Financials Sector
box_df = pd.concat([sp['returns'],AFL['returns'],ALL['returns'],AXP['returns'],AIG['returns'],AMP['returns'],AON['returns'],AJG['returns'],AIZ['returns'],BAC['returns'],BK['returns'],BRKB['returns'],BLK['returns'],COF['returns'],CBOE['returns'],SCHW['returns'],CB['returns'],CINF['returns'],C['returns'],CFG['returns'],CME['returns'],CMA['returns'],DFS['returns'],ETFC['returns'],RE['returns'],FITB['returns'],FRC['returns'],BEN['returns'],GL['returns'],GS['returns'],HIG['returns'],HBAN['returns'],ICE['returns'],IVZ['returns'],JPM['returns'],KEY['returns'],LNC['returns'],L['returns'],MTB['returns'],MKTX['returns'],MMC['returns'],MET['returns'],MCO['returns'],MS['returns'],MSCI['returns'],NDAQ['returns'],NTRS['returns'],PBCT['returns'],PNC['returns'],PFG['returns'],PGR['returns'],PRU['returns'],RJF['returns'],RF['returns'],SPGI['returns'],STT['returns'],SIVB['returns'],SYF['returns'],TROW['returns'],TRV['returns'],TFC['returns'],USB['returns'],UNM['returns'],WRB['returns'],WFC['returns'],WLTW['returns'],ZION['returns']], axis=1)
box_df.columns = ['S&P 500','AFL','ALL','AXP','AIG','AMP','AON','AJG','AIZ','BAC','BK','BRK.B','BLK','COF','CBOE','SCHW','CB','CINF','C','CFG','CME','CMA','DFS','ETFC','RE','FITB','FRC','BEN','GL','GS','HIG','HBAN','ICE','IVZ','JPM','KEY','LNC','L','MTB','MKTX','MMC','MET','MCO','MS','MSCI','NDAQ','NTRS','PBCT','PNC','PFG','PGR','PRU','RJF','RF','SPGI','STT','SIVB','SYF','TROW','TRV','TFC','USB','UNM','WRB','WFC','WLTW','ZION']
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Financials Sector Returns')
Out[493]:
<matplotlib.axes._subplots.AxesSubplot at 0x13fa09125c0>
In [494]:
#Cumulative Returns of Financials Stocks for Whole period

sp['Cumulative Return'].plot(label="S&P 500")
AFL['Cumulative Return'].plot(label='AFL', figsize=(16,8), title = 'Cumulative Return of Financials Stocks')
ALL['Cumulative Return'].plot(label="ALL")
AXP['Cumulative Return'].plot(label="AXP")
AIG['Cumulative Return'].plot(label="AIG")
AMP['Cumulative Return'].plot(label="AMP")
AON['Cumulative Return'].plot(label="AON")
AJG['Cumulative Return'].plot(label="AJG")
AIZ['Cumulative Return'].plot(label="AIZ")
BAC['Cumulative Return'].plot(label="BAC")
BK['Cumulative Return'].plot(label="BK")
BRKB['Cumulative Return'].plot(label="BRK.B")
BLK['Cumulative Return'].plot(label="BLK")
COF['Cumulative Return'].plot(label="COF")
CBOE['Cumulative Return'].plot(label="CBOE")
SCHW['Cumulative Return'].plot(label="SCHW")
CB['Cumulative Return'].plot(label="CB")
CINF['Cumulative Return'].plot(label="CINF")
C['Cumulative Return'].plot(label="C")
CFG['Cumulative Return'].plot(label="CFG")
CME['Cumulative Return'].plot(label="CME")
CMA['Cumulative Return'].plot(label="CMA")
DFS['Cumulative Return'].plot(label="DFS")
ETFC['Cumulative Return'].plot(label="ETFC")
RE['Cumulative Return'].plot(label="RE")
FITB['Cumulative Return'].plot(label="FITB")
FRC['Cumulative Return'].plot(label="FRC")
BEN['Cumulative Return'].plot(label="BEN")
GL['Cumulative Return'].plot(label="GL")
GS['Cumulative Return'].plot(label="GS")
HIG['Cumulative Return'].plot(label="HIG")
HBAN['Cumulative Return'].plot(label="HBAN")
ICE['Cumulative Return'].plot(label="ICE")
IVZ['Cumulative Return'].plot(label="IVZ")
JPM['Cumulative Return'].plot(label="JPM")
KEY['Cumulative Return'].plot(label="KEY")
LNC['Cumulative Return'].plot(label="LNC")
L['Cumulative Return'].plot(label="L")
MTB['Cumulative Return'].plot(label="MTB")
MKTX['Cumulative Return'].plot(label="MKTX")
MMC['Cumulative Return'].plot(label="MMC")
MET['Cumulative Return'].plot(label="MET")
MCO['Cumulative Return'].plot(label="MCO")
MS['Cumulative Return'].plot(label="MS")
MSCI['Cumulative Return'].plot(label="MSCI")
NDAQ['Cumulative Return'].plot(label="NDAQ")
NTRS['Cumulative Return'].plot(label="NTRS")
PBCT['Cumulative Return'].plot(label="PBCT")
PNC['Cumulative Return'].plot(label="PNC")
PFG['Cumulative Return'].plot(label="PFG")
PGR['Cumulative Return'].plot(label="PGR")
PRU['Cumulative Return'].plot(label="PRU")
RJF['Cumulative Return'].plot(label="RJF")
RF['Cumulative Return'].plot(label="RF")
SPGI['Cumulative Return'].plot(label="SPGI")
STT['Cumulative Return'].plot(label="STT")
SIVB['Cumulative Return'].plot(label="SIVB")
SYF['Cumulative Return'].plot(label="SYF")
TROW['Cumulative Return'].plot(label="TROW")
TRV['Cumulative Return'].plot(label="TRV")
TFC['Cumulative Return'].plot(label="TFC")
USB['Cumulative Return'].plot(label="USB")
UNM['Cumulative Return'].plot(label="UNM")
WRB['Cumulative Return'].plot(label="WRB")
WFC['Cumulative Return'].plot(label="WFC")
WLTW['Cumulative Return'].plot(label="WLTW")
ZION['Cumulative Return'].plot(label="ZION")

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [495]:
#Sorting by Cumulative Return


cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],AFL['Cumulative Return'].iloc[-1],ALL['Cumulative Return'].iloc[-1],AXP['Cumulative Return'].iloc[-1],AIG['Cumulative Return'].iloc[-1],AMP['Cumulative Return'].iloc[-1],AON['Cumulative Return'].iloc[-1],AJG['Cumulative Return'].iloc[-1],AIZ['Cumulative Return'].iloc[-1],BAC['Cumulative Return'].iloc[-1],BK['Cumulative Return'].iloc[-1],BRKB['Cumulative Return'].iloc[-1],BLK['Cumulative Return'].iloc[-1],COF['Cumulative Return'].iloc[-1],CBOE['Cumulative Return'].iloc[-1],SCHW['Cumulative Return'].iloc[-1],CB['Cumulative Return'].iloc[-1],CINF['Cumulative Return'].iloc[-1],C['Cumulative Return'].iloc[-1],CFG['Cumulative Return'].iloc[-1],CME['Cumulative Return'].iloc[-1],CMA['Cumulative Return'].iloc[-1],DFS['Cumulative Return'].iloc[-1],ETFC['Cumulative Return'].iloc[-1],RE['Cumulative Return'].iloc[-1],FITB['Cumulative Return'].iloc[-1],FRC['Cumulative Return'].iloc[-1],BEN['Cumulative Return'].iloc[-1],GL['Cumulative Return'].iloc[-1],GS['Cumulative Return'].iloc[-1],HIG['Cumulative Return'].iloc[-1],HBAN['Cumulative Return'].iloc[-1],ICE['Cumulative Return'].iloc[-1],IVZ['Cumulative Return'].iloc[-1],JPM['Cumulative Return'].iloc[-1],KEY['Cumulative Return'].iloc[-1],LNC['Cumulative Return'].iloc[-1],L['Cumulative Return'].iloc[-1],MTB['Cumulative Return'].iloc[-1],MKTX['Cumulative Return'].iloc[-1],MMC['Cumulative Return'].iloc[-1],MET['Cumulative Return'].iloc[-1],MCO['Cumulative Return'].iloc[-1],MS['Cumulative Return'].iloc[-1],MSCI['Cumulative Return'].iloc[-1],NDAQ['Cumulative Return'].iloc[-1],NTRS['Cumulative Return'].iloc[-1],PBCT['Cumulative Return'].iloc[-1],PNC['Cumulative Return'].iloc[-1],PFG['Cumulative Return'].iloc[-1],PGR['Cumulative Return'].iloc[-1],PRU['Cumulative Return'].iloc[-1],RJF['Cumulative Return'].iloc[-1],RF['Cumulative Return'].iloc[-1],SPGI['Cumulative Return'].iloc[-1],STT['Cumulative Return'].iloc[-1],SIVB['Cumulative Return'].iloc[-1],SYF['Cumulative Return'].iloc[-1],TROW['Cumulative Return'].iloc[-1],TRV['Cumulative Return'].iloc[-1],TFC['Cumulative Return'].iloc[-1],USB['Cumulative Return'].iloc[-1],UNM['Cumulative Return'].iloc[-1],WRB['Cumulative Return'].iloc[-1],WFC['Cumulative Return'].iloc[-1],WLTW['Cumulative Return'].iloc[-1],ZION['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],AFLbef['Cumulative Return Before Crash'].iloc[-1],ALLbef['Cumulative Return Before Crash'].iloc[-1],AXPbef['Cumulative Return Before Crash'].iloc[-1],AIGbef['Cumulative Return Before Crash'].iloc[-1],AMPbef['Cumulative Return Before Crash'].iloc[-1],AONbef['Cumulative Return Before Crash'].iloc[-1],AJGbef['Cumulative Return Before Crash'].iloc[-1],AIZbef['Cumulative Return Before Crash'].iloc[-1],BACbef['Cumulative Return Before Crash'].iloc[-1],BKbef['Cumulative Return Before Crash'].iloc[-1],BRKBbef['Cumulative Return Before Crash'].iloc[-1],BLKbef['Cumulative Return Before Crash'].iloc[-1],COFbef['Cumulative Return Before Crash'].iloc[-1],CBOEbef['Cumulative Return Before Crash'].iloc[-1],SCHWbef['Cumulative Return Before Crash'].iloc[-1],CBbef['Cumulative Return Before Crash'].iloc[-1],CINFbef['Cumulative Return Before Crash'].iloc[-1],Cbef['Cumulative Return Before Crash'].iloc[-1],CFGbef['Cumulative Return Before Crash'].iloc[-1],CMEbef['Cumulative Return Before Crash'].iloc[-1],CMAbef['Cumulative Return Before Crash'].iloc[-1],DFSbef['Cumulative Return Before Crash'].iloc[-1],ETFCbef['Cumulative Return Before Crash'].iloc[-1],REbef['Cumulative Return Before Crash'].iloc[-1],FITBbef['Cumulative Return Before Crash'].iloc[-1],FRCbef['Cumulative Return Before Crash'].iloc[-1],BENbef['Cumulative Return Before Crash'].iloc[-1],GLbef['Cumulative Return Before Crash'].iloc[-1],GSbef['Cumulative Return Before Crash'].iloc[-1],HIGbef['Cumulative Return Before Crash'].iloc[-1],HBANbef['Cumulative Return Before Crash'].iloc[-1],ICEbef['Cumulative Return Before Crash'].iloc[-1],IVZbef['Cumulative Return Before Crash'].iloc[-1],JPMbef['Cumulative Return Before Crash'].iloc[-1],KEYbef['Cumulative Return Before Crash'].iloc[-1],LNCbef['Cumulative Return Before Crash'].iloc[-1],Lbef['Cumulative Return Before Crash'].iloc[-1],MTBbef['Cumulative Return Before Crash'].iloc[-1],MKTXbef['Cumulative Return Before Crash'].iloc[-1],MMCbef['Cumulative Return Before Crash'].iloc[-1],METbef['Cumulative Return Before Crash'].iloc[-1],MCObef['Cumulative Return Before Crash'].iloc[-1],MSbef['Cumulative Return Before Crash'].iloc[-1],MSCIbef['Cumulative Return Before Crash'].iloc[-1],NDAQbef['Cumulative Return Before Crash'].iloc[-1],NTRSbef['Cumulative Return Before Crash'].iloc[-1],PBCTbef['Cumulative Return Before Crash'].iloc[-1],PNCbef['Cumulative Return Before Crash'].iloc[-1],PFGbef['Cumulative Return Before Crash'].iloc[-1],PGRbef['Cumulative Return Before Crash'].iloc[-1],PRUbef['Cumulative Return Before Crash'].iloc[-1],RJFbef['Cumulative Return Before Crash'].iloc[-1],RFbef['Cumulative Return Before Crash'].iloc[-1],SPGIbef['Cumulative Return Before Crash'].iloc[-1],STTbef['Cumulative Return Before Crash'].iloc[-1],SIVBbef['Cumulative Return Before Crash'].iloc[-1],SYFbef['Cumulative Return Before Crash'].iloc[-1],TROWbef['Cumulative Return Before Crash'].iloc[-1],TRVbef['Cumulative Return Before Crash'].iloc[-1],TFCbef['Cumulative Return Before Crash'].iloc[-1],USBbef['Cumulative Return Before Crash'].iloc[-1],UNMbef['Cumulative Return Before Crash'].iloc[-1],WRBbef['Cumulative Return Before Crash'].iloc[-1],WFCbef['Cumulative Return Before Crash'].iloc[-1],WLTWbef['Cumulative Return Before Crash'].iloc[-1],ZIONbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],AFLdur['Cumulative Return During Crash'].iloc[-1],ALLdur['Cumulative Return During Crash'].iloc[-1],AXPdur['Cumulative Return During Crash'].iloc[-1],AIGdur['Cumulative Return During Crash'].iloc[-1],AMPdur['Cumulative Return During Crash'].iloc[-1],AONdur['Cumulative Return During Crash'].iloc[-1],AJGdur['Cumulative Return During Crash'].iloc[-1],AIZdur['Cumulative Return During Crash'].iloc[-1],BACdur['Cumulative Return During Crash'].iloc[-1],BKdur['Cumulative Return During Crash'].iloc[-1],BRKBdur['Cumulative Return During Crash'].iloc[-1],BLKdur['Cumulative Return During Crash'].iloc[-1],COFdur['Cumulative Return During Crash'].iloc[-1],CBOEdur['Cumulative Return During Crash'].iloc[-1],SCHWdur['Cumulative Return During Crash'].iloc[-1],CBdur['Cumulative Return During Crash'].iloc[-1],CINFdur['Cumulative Return During Crash'].iloc[-1],Cdur['Cumulative Return During Crash'].iloc[-1],CFGdur['Cumulative Return During Crash'].iloc[-1],CMEdur['Cumulative Return During Crash'].iloc[-1],CMAdur['Cumulative Return During Crash'].iloc[-1],DFSdur['Cumulative Return During Crash'].iloc[-1],ETFCdur['Cumulative Return During Crash'].iloc[-1],REdur['Cumulative Return During Crash'].iloc[-1],FITBdur['Cumulative Return During Crash'].iloc[-1],FRCdur['Cumulative Return During Crash'].iloc[-1],BENdur['Cumulative Return During Crash'].iloc[-1],GLdur['Cumulative Return During Crash'].iloc[-1],GSdur['Cumulative Return During Crash'].iloc[-1],HIGdur['Cumulative Return During Crash'].iloc[-1],HBANdur['Cumulative Return During Crash'].iloc[-1],ICEdur['Cumulative Return During Crash'].iloc[-1],IVZdur['Cumulative Return During Crash'].iloc[-1],JPMdur['Cumulative Return During Crash'].iloc[-1],KEYdur['Cumulative Return During Crash'].iloc[-1],LNCdur['Cumulative Return During Crash'].iloc[-1],Ldur['Cumulative Return During Crash'].iloc[-1],MTBdur['Cumulative Return During Crash'].iloc[-1],MKTXdur['Cumulative Return During Crash'].iloc[-1],MMCdur['Cumulative Return During Crash'].iloc[-1],METdur['Cumulative Return During Crash'].iloc[-1],MCOdur['Cumulative Return During Crash'].iloc[-1],MSdur['Cumulative Return During Crash'].iloc[-1],MSCIdur['Cumulative Return During Crash'].iloc[-1],NDAQdur['Cumulative Return During Crash'].iloc[-1],NTRSdur['Cumulative Return During Crash'].iloc[-1],PBCTdur['Cumulative Return During Crash'].iloc[-1],PNCdur['Cumulative Return During Crash'].iloc[-1],PFGdur['Cumulative Return During Crash'].iloc[-1],PGRdur['Cumulative Return During Crash'].iloc[-1],PRUdur['Cumulative Return During Crash'].iloc[-1],RJFdur['Cumulative Return During Crash'].iloc[-1],RFdur['Cumulative Return During Crash'].iloc[-1],SPGIdur['Cumulative Return During Crash'].iloc[-1],STTdur['Cumulative Return During Crash'].iloc[-1],SIVBdur['Cumulative Return During Crash'].iloc[-1],SYFdur['Cumulative Return During Crash'].iloc[-1],TROWdur['Cumulative Return During Crash'].iloc[-1],TRVdur['Cumulative Return During Crash'].iloc[-1],TFCdur['Cumulative Return During Crash'].iloc[-1],USBdur['Cumulative Return During Crash'].iloc[-1],UNMdur['Cumulative Return During Crash'].iloc[-1],WRBdur['Cumulative Return During Crash'].iloc[-1],WFCdur['Cumulative Return During Crash'].iloc[-1],WLTWdur['Cumulative Return During Crash'].iloc[-1],ZIONdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],AFLaft['Cumulative Return After Crash'].iloc[-1],ALLaft['Cumulative Return After Crash'].iloc[-1],AXPaft['Cumulative Return After Crash'].iloc[-1],AIGaft['Cumulative Return After Crash'].iloc[-1],AMPaft['Cumulative Return After Crash'].iloc[-1],AONaft['Cumulative Return After Crash'].iloc[-1],AJGaft['Cumulative Return After Crash'].iloc[-1],AIZaft['Cumulative Return After Crash'].iloc[-1],BACaft['Cumulative Return After Crash'].iloc[-1],BKaft['Cumulative Return After Crash'].iloc[-1],BRKBaft['Cumulative Return After Crash'].iloc[-1],BLKaft['Cumulative Return After Crash'].iloc[-1],COFaft['Cumulative Return After Crash'].iloc[-1],CBOEaft['Cumulative Return After Crash'].iloc[-1],SCHWaft['Cumulative Return After Crash'].iloc[-1],CBaft['Cumulative Return After Crash'].iloc[-1],CINFaft['Cumulative Return After Crash'].iloc[-1],Caft['Cumulative Return After Crash'].iloc[-1],CFGaft['Cumulative Return After Crash'].iloc[-1],CMEaft['Cumulative Return After Crash'].iloc[-1],CMAaft['Cumulative Return After Crash'].iloc[-1],DFSaft['Cumulative Return After Crash'].iloc[-1],ETFCaft['Cumulative Return After Crash'].iloc[-1],REaft['Cumulative Return After Crash'].iloc[-1],FITBaft['Cumulative Return After Crash'].iloc[-1],FRCaft['Cumulative Return After Crash'].iloc[-1],BENaft['Cumulative Return After Crash'].iloc[-1],GLaft['Cumulative Return After Crash'].iloc[-1],GSaft['Cumulative Return After Crash'].iloc[-1],HIGaft['Cumulative Return After Crash'].iloc[-1],HBANaft['Cumulative Return After Crash'].iloc[-1],ICEaft['Cumulative Return After Crash'].iloc[-1],IVZaft['Cumulative Return After Crash'].iloc[-1],JPMaft['Cumulative Return After Crash'].iloc[-1],KEYaft['Cumulative Return After Crash'].iloc[-1],LNCaft['Cumulative Return After Crash'].iloc[-1],Laft['Cumulative Return After Crash'].iloc[-1],MTBaft['Cumulative Return After Crash'].iloc[-1],MKTXaft['Cumulative Return After Crash'].iloc[-1],MMCaft['Cumulative Return After Crash'].iloc[-1],METaft['Cumulative Return After Crash'].iloc[-1],MCOaft['Cumulative Return After Crash'].iloc[-1],MSaft['Cumulative Return After Crash'].iloc[-1],MSCIaft['Cumulative Return After Crash'].iloc[-1],NDAQaft['Cumulative Return After Crash'].iloc[-1],NTRSaft['Cumulative Return After Crash'].iloc[-1],PBCTaft['Cumulative Return After Crash'].iloc[-1],PNCaft['Cumulative Return After Crash'].iloc[-1],PFGaft['Cumulative Return After Crash'].iloc[-1],PGRaft['Cumulative Return After Crash'].iloc[-1],PRUaft['Cumulative Return After Crash'].iloc[-1],RJFaft['Cumulative Return After Crash'].iloc[-1],RFaft['Cumulative Return After Crash'].iloc[-1],SPGIaft['Cumulative Return After Crash'].iloc[-1],STTaft['Cumulative Return After Crash'].iloc[-1],SIVBaft['Cumulative Return After Crash'].iloc[-1],SYFaft['Cumulative Return After Crash'].iloc[-1],TROWaft['Cumulative Return After Crash'].iloc[-1],TRVaft['Cumulative Return After Crash'].iloc[-1],TFCaft['Cumulative Return After Crash'].iloc[-1],USBaft['Cumulative Return After Crash'].iloc[-1],UNMaft['Cumulative Return After Crash'].iloc[-1],WRBaft['Cumulative Return After Crash'].iloc[-1],WFCaft['Cumulative Return After Crash'].iloc[-1],WLTWaft['Cumulative Return After Crash'].iloc[-1],ZIONaft['Cumulative Return After Crash'].iloc[-1]]}                         

fdf2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','AFL','ALL','AXP','AIG','AMP','AON','AJG','AIZ','BAC','BK','BRK.B','BLK','COF','CBOE','SCHW','CB','CINF','C','CFG','CME','CMA','DFS','ETFC','RE','FITB','FRC','BEN','GL','GS','HIG','HBAN','ICE','IVZ','JPM','KEY','LNC','L','MTB','MKTX','MMC','MET','MCO','MS','MSCI','NDAQ','NTRS','PBCT','PNC','PFG','PGR','PRU','RJF','RF','SPGI','STT','SIVB','SYF','TROW','TRV','TFC','USB','UNM','WRB','WFC','WLTW','ZION'])

pd.DataFrame(fdf2)
Out[495]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
AFL 0.774508 0.983948 0.498932 1.577657
ALL 0.957599 1.120937 0.593148 1.440257
AXP 0.930388 1.158046 0.504906 1.591212
AIG 0.711407 0.974369 0.387550 1.883938
... ... ... ... ...
UNM 0.640345 1.038621 0.344622 1.789017
WRB 0.935952 1.144218 0.555399 1.472786
WFC 0.594853 0.889181 0.533376 1.254257
WLTW 1.070002 1.083920 0.704344 1.401532
ZION 0.786194 0.941763 0.520649 1.603405

67 rows × 4 columns

In [496]:
fdf2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[496]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
LNC 0.834569 1.077507 0.284286 2.724496
DFS 0.724860 0.906254 0.351629 2.274672
SYF 0.680896 0.897545 0.380499 1.993750
PFG 0.891098 1.060227 0.431583 1.947434
FITB 0.791709 1.005393 0.407643 1.931743
... ... ... ... ...
WFC 0.594853 0.889181 0.533376 1.254257
BRK.B 0.918773 1.044734 0.710567 1.237649
AIZ 0.889853 1.096592 0.658190 1.232883
PGR 1.139616 1.125278 0.823231 1.230203
CINF 0.657867 1.072406 0.595612 1.029949

67 rows × 4 columns

In [497]:
#Table of Key Statistics of Financials Stocks
import pandas as pd
import statistics as stats

fmeans = [AFLmean,ALLmean,AXPmean,AIGmean,AMPmean,AONmean,AJGmean,AIZmean,BACmean,BKmean,BRKBmean,BLKmean,COFmean,CBOEmean,SCHWmean,CBmean,CINFmean,Cmean,CFGmean,CMEmean,CMAmean,DFSmean,ETFCmean,REmean,FITBmean,FRCmean,BENmean,GLmean,GSmean,HIGmean,HBANmean,ICEmean,IVZmean,JPMmean,KEYmean,LNCmean,Lmean,MTBmean,MKTXmean,MMCmean,METmean,MCOmean,MSmean,MSCImean,NDAQmean,NTRSmean,PBCTmean,PNCmean,PFGmean,PGRmean,PRUmean,RJFmean,RFmean,SPGImean,STTmean,SIVBmean,SYFmean,TROWmean,TRVmean,TFCmean,USBmean,UNMmean,WRBmean,WFCmean,WLTWmean,ZIONmean]
faveragemean = stats.mean(fmeans)
fvariances = [AFLvar,ALLvar,AXPvar,AIGvar,AMPvar,AONvar,AJGvar,AIZvar,BACvar,BKvar,BRKBvar,BLKvar,COFvar,CBOEvar,SCHWvar,CBvar,CINFvar,Cvar,CFGvar,CMEvar,CMAvar,DFSvar,ETFCvar,REvar,FITBvar,FRCvar,BENvar,GLvar,GSvar,HIGvar,HBANvar,ICEvar,IVZvar,JPMvar,KEYvar,LNCvar,Lvar,MTBvar,MKTXvar,MMCvar,METvar,MCOvar,MSvar,MSCIvar,NDAQvar,NTRSvar,PBCTvar,PNCvar,PFGvar,PGRvar,PRUvar,RJFvar,RFvar,SPGIvar,STTvar,SIVBvar,SYFvar,TROWvar,TRVvar,TFCvar,USBvar,UNMvar,WRBvar,WFCvar,WLTWvar,ZIONvar]
faveragevar = stats.mean(fvariances)
fkurtoses = [AFLkurt,ALLkurt,AXPkurt,AIGkurt,AMPkurt,AONkurt,AJGkurt,AIZkurt,BACkurt,BKkurt,BRKBkurt,BLKkurt,COFkurt,CBOEkurt,SCHWkurt,CBkurt,CINFkurt,Ckurt,CFGkurt,CMEkurt,CMAkurt,DFSkurt,ETFCkurt,REkurt,FITBkurt,FRCkurt,BENkurt,GLkurt,GSkurt,HIGkurt,HBANkurt,ICEkurt,IVZkurt,JPMkurt,KEYkurt,LNCkurt,Lkurt,MTBkurt,MKTXkurt,MMCkurt,METkurt,MCOkurt,MSkurt,MSCIkurt,NDAQkurt,NTRSkurt,PBCTkurt,PNCkurt,PFGkurt,PGRkurt,PRUkurt,RJFkurt,RFkurt,SPGIkurt,STTkurt,SIVBkurt,SYFkurt,TROWkurt,TRVkurt,TFCkurt,USBkurt,UNMkurt,WRBkurt,WFCkurt,WLTWkurt,ZIONkurt]
faveragekurt = stats.mean(fkurtoses)
fskews = [AFLskew,ALLskew,AXPskew,AIGskew,AMPskew,AONskew,AJGskew,AIZskew,BACskew,BKskew,BRKBskew,BLKskew,COFskew,CBOEskew,SCHWskew,CBskew,CINFskew,Cskew,CFGskew,CMEskew,CMAskew,DFSskew,ETFCskew,REskew,FITBskew,FRCskew,BENskew,GLskew,GSskew,HIGskew,HBANskew,ICEskew,IVZskew,JPMskew,KEYskew,LNCskew,Lskew,MTBskew,MKTXskew,MMCskew,METskew,MCOskew,MSskew,MSCIskew,NDAQskew,NTRSskew,PBCTskew,PNCskew,PFGskew,PGRskew,PRUskew,RJFskew,RFskew,SPGIskew,STTskew,SIVBskew,SYFskew,TROWskew,TRVskew,TFCskew,USBskew,UNMskew,WRBskew,WFCskew,WLTWskew,ZIONskew]
faverageskew = stats.mean(fskews)

Financials = {'Mean': [[spmean],[faveragemean],[AFLmean], [ALLmean],[AXPmean],[AIGmean],[AMPmean],[AONmean],[AJGmean],[AIZmean],[BACmean],[BKmean],[BRKBmean],[BLKmean],[COFmean],[CBOEmean],[SCHWmean],[CBmean],[CINFmean],[Cmean],[CFGmean],[CMEmean],[CMAmean],[DFSmean],[ETFCmean],[REmean],[FITBmean],[FRCmean],[BENmean],[GLmean], [GSmean],[HIGmean],[HBANmean],[ICEmean],[IVZmean],[JPMmean],[KEYmean],[LNCmean],[Lmean],[MTBmean],[MKTXmean],[MMCmean],[METmean],[MCOmean],[MSmean],[MSCImean],[NDAQmean],[NTRSmean],[PBCTmean],[PNCmean],[PFGmean],[PGRmean],[PRUmean],[RJFmean],[RFmean],[SPGImean],[STTmean],[SIVBmean],[SYFmean],[TROWmean],[TRVmean],[TFCmean],[USBmean],[UNMmean],[WRBmean],[WFCmean],[WLTWmean],[ZIONmean]], 
        'Variance': [[spvar],[faveragevar],[AFLvar],[ALLvar],[AXPvar],[AIGvar],[AMPvar],[AONvar],[AJGvar],[AIZvar],[BACvar],[BKvar],[BRKBvar],[BLKvar],[COFvar],[CBOEvar],[SCHWvar],[CBvar],[CINFvar],[Cvar],[CFGvar],[CMEvar],[CMAvar],[DFSvar],[ETFCvar],[REvar],[FITBvar],[FRCvar],[BENvar],[GLvar],[GSvar],[HIGvar],[HBANvar],[ICEvar],[IVZvar],[JPMvar],[KEYvar],[LNCvar],[Lvar],[MTBvar],[MKTXvar],[MMCvar],[METvar],[MCOvar],[MSvar],[MSCIvar],[NDAQvar],[NTRSvar],[PBCTvar],[PNCvar],[PFGvar],[PGRvar],[PRUvar],[RJFvar],[RFvar],[SPGIvar],[STTvar],[SIVBvar],[SYFvar],[TROWvar],[TRVvar],[TFCvar],[USBvar],[UNMvar],[WRBvar],[WFCvar],[WLTWvar],[ZIONvar]], 
          'Kurtosis': [[spkurt],[faveragekurt],[AFLkurt],[ALLkurt],[AXPkurt],[AIGkurt],[AMPkurt],[AONkurt],[AJGkurt],[AIZkurt],[BACkurt],[BKkurt],[BRKBkurt],[BLKkurt],[COFkurt],[CBOEkurt],[SCHWkurt],[CBkurt],[CINFkurt],[Ckurt],[CFGkurt],[CMEkurt],[CMAkurt],[DFSkurt],[ETFCkurt],[REkurt],[FITBkurt],[FRCkurt],[BENkurt],[GLkurt],[GSkurt],[HIGkurt],[HBANkurt],[ICEkurt],[IVZkurt],[JPMkurt],[KEYkurt],[LNCkurt],[Lkurt],[MTBkurt],[MKTXkurt],[MMCkurt],[METkurt],[MCOkurt],[MSkurt],[MSCIkurt],[NDAQkurt],[NTRSkurt],[PBCTkurt],[PNCkurt],[PFGkurt],[PGRkurt],[PRUkurt],[RJFkurt],[RFkurt],[SPGIkurt],[STTkurt],[SIVBkurt],[SYFkurt],[TROWkurt],[TRVkurt],[TFCkurt],[USBkurt],[UNMkurt],[WRBkurt],[WFCkurt],[WLTWkurt],[ZIONkurt]],
          'Skew': [[spskew],[faverageskew],[AFLskew],[ALLskew],[AXPskew],[AIGskew],[AMPskew],[AONskew],[AJGskew],[AIZskew],[BACskew],[BKskew],[BRKBskew],[BLKskew],[COFskew],[CBOEskew],[SCHWskew],[CBskew],[CINFskew],[Cskew],[CFGskew],[CMEskew],[CMAskew],[DFSskew],[ETFCskew],[REskew],[FITBskew],[FRCskew],[BENskew],[GLskew],[GSskew],[HIGskew],[HBANskew],[ICEskew],[IVZskew],[JPMskew],[KEYskew],[LNCskew],[Lskew],[MTBskew],[MKTXskew],[MMCskew],[METskew],[MCOskew],[MSskew],[MSCIskew],[NDAQskew],[NTRSskew],[PBCTskew],[PNCskew],[PFGskew],[PGRskew],[PRUskew],[RJFskew],[RFskew],[SPGIskew],[STTskew],[SIVBskew],[SYFskew],[TROWskew],[TRVskew],[TFCskew],[USBskew],[UNMskew],[WRBskew],[WFCskew],[WLTWskew],[ZIONskew]],
        }

fdf = pd.DataFrame(Financials, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','AFL','ALL','AXP','AIG','AMP','AONcon','AJG','AIZ','BAC','BK','BRK.B','BLK','COF','CBOE','SCHW','CB','CINF','C','CFG','CME','CMA','DFS','ETFC','RE','FITB','FRC','BEN','GL','GS','HIG','HBAN','ICE','IVZ','JPM','KEY','LNC','L','MTB','MKTX','MMC','MET','MCO','MS','MSCI','NDAQ','NTRS','PBCT','PNC','PFG','PGR','PRU','RJF','RF','SPGI','STT','SIVB','SYF','TROW','TRV','TFC','USB','UNM','WRB','WFC','WLTW','ZION']
)

pd.DataFrame(fdf)
Out[497]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [2.515872452493875e-05] [0.0020734337246651737] [4.282598466014067] [-0.00011166298689266822]
AFL [-0.000967836273363166] [0.002156690601692281] [10.212729846121533] [0.7849652535110613]
ALL [0.0002574892742013509] [0.0011948773462833178] [3.339097238058569] [-0.4947732157867784]
AXP [0.0006279589388251375] [0.0024657014269278076] [4.782697183390513] [0.7940939614124277]
... ... ... ... ...
UNM [-0.0017259479172923816] [0.003576269796185367] [4.546947169232652] [-0.09726652244870562]
WRB [0.0001996016258902557] [0.0014449657483341562] [3.2748338148644107] [-0.28978749480636434]
WFC [-0.0030752714881652652] [0.002091063422304439] [2.5055014198216226] [0.22284703336789266]
WLTW [0.0010307779561138392] [0.0010034432894362984] [5.65492387716282] [0.5263556786227973]
ZION [-0.001225762139083486] [0.0013755125943388142] [1.3186840168536182] [0.19933412922103944]

68 rows × 4 columns

In [498]:
fdf.sort_values("Mean", ascending=False)
Out[498]:
Mean Variance Kurtosis Skew
MSCI [0.0027480580152483935] [0.0016342520320978108] [4.188591048249797] [0.434137658502939]
MCO [0.0025031142915414887] [0.0016917779260679838] [4.643734056517664] [0.16322013982791034]
SPGI [0.0024186244756401803] [0.0013971750803994866] [4.505391291106385] [-0.22365483497546004]
MKTX [0.00240593764562912] [0.0012521570417440805] [3.805021985065183] [0.7806689285116256]
ETFC [0.00228427546728685] [0.0027155350841105716] [5.572154303619512] [0.6664739065749334]
... ... ... ... ...
UNM [-0.0017259479172923816] [0.003576269796185367] [4.546947169232652] [-0.09726652244870562]
CMA [-0.0018504781045530015] [0.0030902306223662317] [2.708923670524708] [-0.42442981900151877]
IVZ [-0.002122473921134098] [0.0032372589315144335] [2.76025085734707] [-0.44070623432902484]
CINF [-0.002351792735105642] [0.0019443513492355682] [2.490474684037139] [0.18030001898863882]
WFC [-0.0030752714881652652] [0.002091063422304439] [2.5055014198216226] [0.22284703336789266]

68 rows × 4 columns

Information Technology

In [499]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ACN = web.DataReader('ACN', 'yahoo', start, end)
ACN['returns'] = ACN['Close'].pct_change(1)
ACN['Cumulative Return'] = (1 + ACN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ACNmean = ACN['returns'].mean()
ACNvar = ACN['returns'].var()
ACNkurt = ACN['returns'].kurt()
ACNskew = ACN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ACNbef = web.DataReader('ACN', 'yahoo', start, crashstart)
ACNbef['avgreturnbefore'] = ACNbef['Close'].pct_change(1)
ACNbef['Cumulative Return Before Crash'] = (1 + ACNbef['avgreturnbefore']).cumprod()

#period of crash
ACNdur = web.DataReader('ACN', 'yahoo', crashstart, crashend)
ACNdur['avgreturnduring'] = ACNdur['Close'].pct_change(1)
ACNdur['Cumulative Return During Crash'] = (1 + ACNdur['avgreturnduring']).cumprod()

#period after crash
ACNaft = web.DataReader('ACN', 'yahoo', crashend, end)
ACNaft['avgreturnafter'] = ACNaft['Close'].pct_change(1)
ACNaft['Cumulative Return After Crash'] = (1 + ACNaft['avgreturnafter']).cumprod()
In [500]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ADBE = web.DataReader('ADBE', 'yahoo', start, end)
ADBE['returns'] = ADBE['Close'].pct_change(1)
ADBE['Cumulative Return'] = (1 + ADBE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ADBEmean = ADBE['returns'].mean()
ADBEvar = ADBE['returns'].var()
ADBEkurt = ADBE['returns'].kurt()
ADBEskew = ADBE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ADBEbef = web.DataReader('ADBE', 'yahoo', start, crashstart)
ADBEbef['avgreturnbefore'] = ADBEbef['Close'].pct_change(1)
ADBEbef['Cumulative Return Before Crash'] = (1 + ADBEbef['avgreturnbefore']).cumprod()

#period of crash
ADBEdur = web.DataReader('ADBE', 'yahoo', crashstart, crashend)
ADBEdur['avgreturnduring'] = ADBEdur['Close'].pct_change(1)
ADBEdur['Cumulative Return During Crash'] = (1 + ADBEdur['avgreturnduring']).cumprod()

#period after crash
ADBEaft = web.DataReader('ADBE', 'yahoo', crashend, end)
ADBEaft['avgreturnafter'] = ADBEaft['Close'].pct_change(1)
ADBEaft['Cumulative Return After Crash'] = (1 + ADBEaft['avgreturnafter']).cumprod()
In [501]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AMD = web.DataReader('AMD', 'yahoo', start, end)
AMD['returns'] = AMD['Close'].pct_change(1)
AMD['Cumulative Return'] = (1 + AMD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AMDmean = AMD['returns'].mean()
AMDvar = AMD['returns'].var()
AMDkurt = AMD['returns'].kurt()
AMDskew = AMD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AMDbef = web.DataReader('AMD', 'yahoo', start, crashstart)
AMDbef['avgreturnbefore'] = AMDbef['Close'].pct_change(1)
AMDbef['Cumulative Return Before Crash'] = (1 + AMDbef['avgreturnbefore']).cumprod()

#period of crash
AMDdur = web.DataReader('AMD', 'yahoo', crashstart, crashend)
AMDdur['avgreturnduring'] = AMDdur['Close'].pct_change(1)
AMDdur['Cumulative Return During Crash'] = (1 + AMDdur['avgreturnduring']).cumprod()

#period after crash
AMDaft = web.DataReader('AMD', 'yahoo', crashend, end)
AMDaft['avgreturnafter'] = AMDaft['Close'].pct_change(1)
AMDaft['Cumulative Return After Crash'] = (1 + AMDaft['avgreturnafter']).cumprod()
In [502]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AKAM = web.DataReader('AKAM', 'yahoo', start, end)
AKAM['returns'] = AKAM['Close'].pct_change(1)
AKAM['Cumulative Return'] = (1 + AKAM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AKAMmean = AKAM['returns'].mean()
AKAMvar = AKAM['returns'].var()
AKAMkurt = AKAM['returns'].kurt()
AKAMskew = AKAM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AKAMbef = web.DataReader('AKAM', 'yahoo', start, crashstart)
AKAMbef['avgreturnbefore'] = AKAMbef['Close'].pct_change(1)
AKAMbef['Cumulative Return Before Crash'] = (1 + AKAMbef['avgreturnbefore']).cumprod()

#period of crash
AKAMdur = web.DataReader('AKAM', 'yahoo', crashstart, crashend)
AKAMdur['avgreturnduring'] = AKAMdur['Close'].pct_change(1)
AKAMdur['Cumulative Return During Crash'] = (1 + AKAMdur['avgreturnduring']).cumprod()

#period after crash
AKAMaft = web.DataReader('AKAM', 'yahoo', crashend, end)
AKAMaft['avgreturnafter'] = AKAMaft['Close'].pct_change(1)
AKAMaft['Cumulative Return After Crash'] = (1 + AKAMaft['avgreturnafter']).cumprod()
In [503]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ADS = web.DataReader('ADS', 'yahoo', start, end)
ADS['returns'] = ADS['Close'].pct_change(1)
ADS['Cumulative Return'] = (1 + ADS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ADSmean = ADS['returns'].mean()
ADSvar = ADS['returns'].var()
ADSkurt = ADS['returns'].kurt()
ADSskew = ADS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ADSbef = web.DataReader('ADS', 'yahoo', start, crashstart)
ADSbef['avgreturnbefore'] = ADSbef['Close'].pct_change(1)
ADSbef['Cumulative Return Before Crash'] = (1 + ADSbef['avgreturnbefore']).cumprod()

#period of crash
ADSdur = web.DataReader('ADS', 'yahoo', crashstart, crashend)
ADSdur['avgreturnduring'] = ADSdur['Close'].pct_change(1)
ADSdur['Cumulative Return During Crash'] = (1 + ADSdur['avgreturnduring']).cumprod()

#period after crash
ADSaft = web.DataReader('ADS', 'yahoo', crashend, end)
ADSaft['avgreturnafter'] = ADSaft['Close'].pct_change(1)
ADSaft['Cumulative Return After Crash'] = (1 + ADSaft['avgreturnafter']).cumprod()
In [504]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
APH = web.DataReader('APH', 'yahoo', start, end)
APH['returns'] = APH['Close'].pct_change(1)
APH['Cumulative Return'] = (1 + APH['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
APHmean = APH['returns'].mean()
APHvar = APH['returns'].var()
APHkurt = APH['returns'].kurt()
APHskew = APH['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
APHbef = web.DataReader('APH', 'yahoo', start, crashstart)
APHbef['avgreturnbefore'] = APHbef['Close'].pct_change(1)
APHbef['Cumulative Return Before Crash'] = (1 + APHbef['avgreturnbefore']).cumprod()

#period of crash
APHdur = web.DataReader('APH', 'yahoo', crashstart, crashend)
APHdur['avgreturnduring'] = APHdur['Close'].pct_change(1)
APHdur['Cumulative Return During Crash'] = (1 + APHdur['avgreturnduring']).cumprod()

#period after crash
APHaft = web.DataReader('APH', 'yahoo', crashend, end)
APHaft['avgreturnafter'] = APHaft['Close'].pct_change(1)
APHaft['Cumulative Return After Crash'] = (1 + APHaft['avgreturnafter']).cumprod()
In [505]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ADI = web.DataReader('ADI', 'yahoo', start, end)
ADI['returns'] = ADI['Close'].pct_change(1)
ADI['Cumulative Return'] = (1 + ADI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ADImean = ADI['returns'].mean()
ADIvar = ADI['returns'].var()
ADIkurt = ADI['returns'].kurt()
ADIskew = ADI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ADIbef = web.DataReader('ADI', 'yahoo', start, crashstart)
ADIbef['avgreturnbefore'] = ADIbef['Close'].pct_change(1)
ADIbef['Cumulative Return Before Crash'] = (1 + ADIbef['avgreturnbefore']).cumprod()

#period of crash
ADIdur = web.DataReader('ADI', 'yahoo', crashstart, crashend)
ADIdur['avgreturnduring'] = ADIdur['Close'].pct_change(1)
ADIdur['Cumulative Return During Crash'] = (1 + ADIdur['avgreturnduring']).cumprod()

#period after crash
ADIaft = web.DataReader('ADI', 'yahoo', crashend, end)
ADIaft['avgreturnafter'] = ADIaft['Close'].pct_change(1)
ADIaft['Cumulative Return After Crash'] = (1 + ADIaft['avgreturnafter']).cumprod()
In [506]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ANSS = web.DataReader('ANSS', 'yahoo', start, end)
ANSS['returns'] = ANSS['Close'].pct_change(1)
ANSS['Cumulative Return'] = (1 + ANSS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ANSSmean = ANSS['returns'].mean()
ANSSvar = ANSS['returns'].var()
ANSSkurt = ANSS['returns'].kurt()
ANSSskew = ANSS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ANSSbef = web.DataReader('ANSS', 'yahoo', start, crashstart)
ANSSbef['avgreturnbefore'] = ANSSbef['Close'].pct_change(1)
ANSSbef['Cumulative Return Before Crash'] = (1 + ANSSbef['avgreturnbefore']).cumprod()

#period of crash
ANSSdur = web.DataReader('ANSS', 'yahoo', crashstart, crashend)
ANSSdur['avgreturnduring'] = ANSSdur['Close'].pct_change(1)
ANSSdur['Cumulative Return During Crash'] = (1 + ANSSdur['avgreturnduring']).cumprod()

#period after crash
ANSSaft = web.DataReader('ANSS', 'yahoo', crashend, end)
ANSSaft['avgreturnafter'] = ANSSaft['Close'].pct_change(1)
ANSSaft['Cumulative Return After Crash'] = (1 + ANSSaft['avgreturnafter']).cumprod()
In [507]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AAPL = web.DataReader('AAPL', 'yahoo', start, end)
AAPL['returns'] = AAPL['Close'].pct_change(1)
AAPL['Cumulative Return'] = (1 + AAPL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AAPLmean = AAPL['returns'].mean()
AAPLvar = AAPL['returns'].var()
AAPLkurt = AAPL['returns'].kurt()
AAPLskew = AAPL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AAPLbef = web.DataReader('AAPL', 'yahoo', start, crashstart)
AAPLbef['avgreturnbefore'] = AAPLbef['Close'].pct_change(1)
AAPLbef['Cumulative Return Before Crash'] = (1 + AAPLbef['avgreturnbefore']).cumprod()

#period of crash
AAPLdur = web.DataReader('AAPL', 'yahoo', crashstart, crashend)
AAPLdur['avgreturnduring'] = AAPLdur['Close'].pct_change(1)
AAPLdur['Cumulative Return During Crash'] = (1 + AAPLdur['avgreturnduring']).cumprod()

#period after crash
AAPLaft = web.DataReader('AAPL', 'yahoo', crashend, end)
AAPLaft['avgreturnafter'] = AAPLaft['Close'].pct_change(1)
AAPLaft['Cumulative Return After Crash'] = (1 + AAPLaft['avgreturnafter']).cumprod()
In [508]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AMAT = web.DataReader('AMAT', 'yahoo', start, end)
AMAT['returns'] = AMAT['Close'].pct_change(1)
AMAT['Cumulative Return'] = (1 + AMAT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AMATmean = AMAT['returns'].mean()
AMATvar = AMAT['returns'].var()
AMATkurt = AMAT['returns'].kurt()
AMATskew = AMAT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AMATbef = web.DataReader('AMAT', 'yahoo', start, crashstart)
AMATbef['avgreturnbefore'] = AMATbef['Close'].pct_change(1)
AMATbef['Cumulative Return Before Crash'] = (1 + AMATbef['avgreturnbefore']).cumprod()

#period of crash
AMATdur = web.DataReader('AMAT', 'yahoo', crashstart, crashend)
AMATdur['avgreturnduring'] = AMATdur['Close'].pct_change(1)
AMATdur['Cumulative Return During Crash'] = (1 + AMATdur['avgreturnduring']).cumprod()

#period after crash
AMATaft = web.DataReader('AMAT', 'yahoo', crashend, end)
AMATaft['avgreturnafter'] = AMATaft['Close'].pct_change(1)
AMATaft['Cumulative Return After Crash'] = (1 + AMATaft['avgreturnafter']).cumprod()
In [509]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ANET = web.DataReader('ANET', 'yahoo', start, end)
ANET['returns'] = ANET['Close'].pct_change(1)
ANET['Cumulative Return'] = (1 + ANET['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ANETmean = ANET['returns'].mean()
ANETvar = ANET['returns'].var()
ANETkurt = ANET['returns'].kurt()
ANETskew = ANET['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ANETbef = web.DataReader('ANET', 'yahoo', start, crashstart)
ANETbef['avgreturnbefore'] = ANETbef['Close'].pct_change(1)
ANETbef['Cumulative Return Before Crash'] = (1 + ANETbef['avgreturnbefore']).cumprod()

#period of crash
ANETdur = web.DataReader('ANET', 'yahoo', crashstart, crashend)
ANETdur['avgreturnduring'] = ANETdur['Close'].pct_change(1)
ANETdur['Cumulative Return During Crash'] = (1 + ANETdur['avgreturnduring']).cumprod()

#period after crash
ANETaft = web.DataReader('ANET', 'yahoo', crashend, end)
ANETaft['avgreturnafter'] = ANETaft['Close'].pct_change(1)
ANETaft['Cumulative Return After Crash'] = (1 + ANETaft['avgreturnafter']).cumprod()
In [510]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ADSK = web.DataReader('ADSK', 'yahoo', start, end)
ADSK['returns'] = ADSK['Close'].pct_change(1)
ADSK['Cumulative Return'] = (1 + ADSK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ADSKmean = ADSK['returns'].mean()
ADSKvar = ADSK['returns'].var()
ADSKkurt = ADSK['returns'].kurt()
ADSKskew = ADSK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ADSKbef = web.DataReader('ADSK', 'yahoo', start, crashstart)
ADSKbef['avgreturnbefore'] = ADSKbef['Close'].pct_change(1)
ADSKbef['Cumulative Return Before Crash'] = (1 + ADSKbef['avgreturnbefore']).cumprod()

#period of crash
ADSKdur = web.DataReader('ADSK', 'yahoo', crashstart, crashend)
ADSKdur['avgreturnduring'] = ADSKdur['Close'].pct_change(1)
ADSKdur['Cumulative Return During Crash'] = (1 + ADSKdur['avgreturnduring']).cumprod()

#period after crash
ADSKaft = web.DataReader('ADSK', 'yahoo', crashend, end)
ADSKaft['avgreturnafter'] = ADSKaft['Close'].pct_change(1)
ADSKaft['Cumulative Return After Crash'] = (1 + ADSKaft['avgreturnafter']).cumprod()
In [511]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ADP = web.DataReader('ADP', 'yahoo', start, end)
ADP['returns'] = ADP['Close'].pct_change(1)
ADP['Cumulative Return'] = (1 + ADP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ADPmean = ADP['returns'].mean()
ADPvar = ADP['returns'].var()
ADPkurt = ADP['returns'].kurt()
ADPskew = ADP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ADPbef = web.DataReader('ADP', 'yahoo', start, crashstart)
ADPbef['avgreturnbefore'] = ADPbef['Close'].pct_change(1)
ADPbef['Cumulative Return Before Crash'] = (1 + ADPbef['avgreturnbefore']).cumprod()

#period of crash
ADPdur = web.DataReader('ADP', 'yahoo', crashstart, crashend)
ADPdur['avgreturnduring'] = ADPdur['Close'].pct_change(1)
ADPdur['Cumulative Return During Crash'] = (1 + ADPdur['avgreturnduring']).cumprod()

#period after crash
ADPaft = web.DataReader('ADP', 'yahoo', crashend, end)
ADPaft['avgreturnafter'] = ADPaft['Close'].pct_change(1)
ADPaft['Cumulative Return After Crash'] = (1 + ADPaft['avgreturnafter']).cumprod()
In [512]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AVGO = web.DataReader('AVGO', 'yahoo', start, end)
AVGO['returns'] = AVGO['Close'].pct_change(1)
AVGO['Cumulative Return'] = (1 + AVGO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AVGOmean = AVGO['returns'].mean()
AVGOvar = AVGO['returns'].var()
AVGOkurt = AVGO['returns'].kurt()
AVGOskew = AVGO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AVGObef = web.DataReader('AVGO', 'yahoo', start, crashstart)
AVGObef['avgreturnbefore'] = AVGObef['Close'].pct_change(1)
AVGObef['Cumulative Return Before Crash'] = (1 + AVGObef['avgreturnbefore']).cumprod()

#period of crash
AVGOdur = web.DataReader('AVGO', 'yahoo', crashstart, crashend)
AVGOdur['avgreturnduring'] = AVGOdur['Close'].pct_change(1)
AVGOdur['Cumulative Return During Crash'] = (1 + AVGOdur['avgreturnduring']).cumprod()

#period after crash
AVGOaft = web.DataReader('AVGO', 'yahoo', crashend, end)
AVGOaft['avgreturnafter'] = AVGOaft['Close'].pct_change(1)
AVGOaft['Cumulative Return After Crash'] = (1 + AVGOaft['avgreturnafter']).cumprod()
In [513]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BR = web.DataReader('BR', 'yahoo', start, end)
BR['returns'] = BR['Close'].pct_change(1)
BR['Cumulative Return'] = (1 + BR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BRmean = BR['returns'].mean()
BRvar = BR['returns'].var()
BRkurt = BR['returns'].kurt()
BRskew = BR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BRbef = web.DataReader('BR', 'yahoo', start, crashstart)
BRbef['avgreturnbefore'] = BRbef['Close'].pct_change(1)
BRbef['Cumulative Return Before Crash'] = (1 + BRbef['avgreturnbefore']).cumprod()

#period of crash
BRdur = web.DataReader('BR', 'yahoo', crashstart, crashend)
BRdur['avgreturnduring'] = BRdur['Close'].pct_change(1)
BRdur['Cumulative Return During Crash'] = (1 + BRdur['avgreturnduring']).cumprod()

#period after crash
BRaft = web.DataReader('BR', 'yahoo', crashend, end)
BRaft['avgreturnafter'] = BRaft['Close'].pct_change(1)
BRaft['Cumulative Return After Crash'] = (1 + BRaft['avgreturnafter']).cumprod()
In [514]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CDNS = web.DataReader('CDNS', 'yahoo', start, end)
CDNS['returns'] = CDNS['Close'].pct_change(1)
CDNS['Cumulative Return'] = (1 + CDNS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CDNSmean = CDNS['returns'].mean()
CDNSvar = CDNS['returns'].var()
CDNSkurt = CDNS['returns'].kurt()
CDNSskew = CDNS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CDNSbef = web.DataReader('CDNS', 'yahoo', start, crashstart)
CDNSbef['avgreturnbefore'] = CDNSbef['Close'].pct_change(1)
CDNSbef['Cumulative Return Before Crash'] = (1 + CDNSbef['avgreturnbefore']).cumprod()

#period of crash
CDNSdur = web.DataReader('CDNS', 'yahoo', crashstart, crashend)
CDNSdur['avgreturnduring'] = CDNSdur['Close'].pct_change(1)
CDNSdur['Cumulative Return During Crash'] = (1 + CDNSdur['avgreturnduring']).cumprod()

#period after crash
CDNSaft = web.DataReader('CDNS', 'yahoo', crashend, end)
CDNSaft['avgreturnafter'] = CDNSaft['Close'].pct_change(1)
CDNSaft['Cumulative Return After Crash'] = (1 + CDNSaft['avgreturnafter']).cumprod()
In [515]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CDW = web.DataReader('CDW', 'yahoo', start, end)
CDW['returns'] = CDW['Close'].pct_change(1)
CDW['Cumulative Return'] = (1 + CDW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CDWmean = CDW['returns'].mean()
CDWvar = CDW['returns'].var()
CDWkurt = CDW['returns'].kurt()
CDWskew = CDW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CDWbef = web.DataReader('CDW', 'yahoo', start, crashstart)
CDWbef['avgreturnbefore'] = CDWbef['Close'].pct_change(1)
CDWbef['Cumulative Return Before Crash'] = (1 + CDWbef['avgreturnbefore']).cumprod()

#period of crash
CDWdur = web.DataReader('CDW', 'yahoo', crashstart, crashend)
CDWdur['avgreturnduring'] = CDWdur['Close'].pct_change(1)
CDWdur['Cumulative Return During Crash'] = (1 + CDWdur['avgreturnduring']).cumprod()

#period after crash
CDWaft = web.DataReader('CDW', 'yahoo', crashend, end)
CDWaft['avgreturnafter'] = CDWaft['Close'].pct_change(1)
CDWaft['Cumulative Return After Crash'] = (1 + CDWaft['avgreturnafter']).cumprod()
In [516]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CSCO = web.DataReader('CSCO', 'yahoo', start, end)
CSCO['returns'] = CSCO['Close'].pct_change(1)
CSCO['Cumulative Return'] = (1 + CSCO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CSCOmean = CSCO['returns'].mean()
CSCOvar = CSCO['returns'].var()
CSCOkurt = CSCO['returns'].kurt()
CSCOskew = CSCO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CSCObef = web.DataReader('CSCO', 'yahoo', start, crashstart)
CSCObef['avgreturnbefore'] = CSCObef['Close'].pct_change(1)
CSCObef['Cumulative Return Before Crash'] = (1 + CSCObef['avgreturnbefore']).cumprod()

#period of crash
CSCOdur = web.DataReader('CSCO', 'yahoo', crashstart, crashend)
CSCOdur['avgreturnduring'] = CSCOdur['Close'].pct_change(1)
CSCOdur['Cumulative Return During Crash'] = (1 + CSCOdur['avgreturnduring']).cumprod()

#period after crash
CSCOaft = web.DataReader('CSCO', 'yahoo', crashend, end)
CSCOaft['avgreturnafter'] = CSCOaft['Close'].pct_change(1)
CSCOaft['Cumulative Return After Crash'] = (1 + CSCOaft['avgreturnafter']).cumprod()
In [517]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CTXS = web.DataReader('CTXS', 'yahoo', start, end)
CTXS['returns'] = CTXS['Close'].pct_change(1)
CTXS['Cumulative Return'] = (1 + CTXS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CTXSmean = CTXS['returns'].mean()
CTXSvar = CTXS['returns'].var()
CTXSkurt = CTXS['returns'].kurt()
CTXSskew = CTXS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CTXSbef = web.DataReader('CTXS', 'yahoo', start, crashstart)
CTXSbef['avgreturnbefore'] = CTXSbef['Close'].pct_change(1)
CTXSbef['Cumulative Return Before Crash'] = (1 + CTXSbef['avgreturnbefore']).cumprod()

#period of crash
CTXSdur = web.DataReader('CTXS', 'yahoo', crashstart, crashend)
CTXSdur['avgreturnduring'] = CTXSdur['Close'].pct_change(1)
CTXSdur['Cumulative Return During Crash'] = (1 + CTXSdur['avgreturnduring']).cumprod()

#period after crash
CTXSaft = web.DataReader('CTXS', 'yahoo', crashend, end)
CTXSaft['avgreturnafter'] = CTXSaft['Close'].pct_change(1)
CTXSaft['Cumulative Return After Crash'] = (1 + CTXSaft['avgreturnafter']).cumprod()
In [518]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CTSH = web.DataReader('CTSH', 'yahoo', start, end)
CTSH['returns'] = CTSH['Close'].pct_change(1)
CTSH['Cumulative Return'] = (1 + CTSH['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CTSHmean = CTSH['returns'].mean()
CTSHvar = CTSH['returns'].var()
CTSHkurt = CTSH['returns'].kurt()
CTSHskew = CTSH['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CTSHbef = web.DataReader('CTSH', 'yahoo', start, crashstart)
CTSHbef['avgreturnbefore'] = CTSHbef['Close'].pct_change(1)
CTSHbef['Cumulative Return Before Crash'] = (1 + CTSHbef['avgreturnbefore']).cumprod()

#period of crash
CTSHdur = web.DataReader('CTSH', 'yahoo', crashstart, crashend)
CTSHdur['avgreturnduring'] = CTSHdur['Close'].pct_change(1)
CTSHdur['Cumulative Return During Crash'] = (1 + CTSHdur['avgreturnduring']).cumprod()

#period after crash
CTSHaft = web.DataReader('CTSH', 'yahoo', crashend, end)
CTSHaft['avgreturnafter'] = CTSHaft['Close'].pct_change(1)
CTSHaft['Cumulative Return After Crash'] = (1 + CTSHaft['avgreturnafter']).cumprod()
In [519]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GLW = web.DataReader('GLW', 'yahoo', start, end)
GLW['returns'] = GLW['Close'].pct_change(1)
GLW['Cumulative Return'] = (1 + GLW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GLWmean = GLW['returns'].mean()
GLWvar = GLW['returns'].var()
GLWkurt = GLW['returns'].kurt()
GLWskew = GLW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GLWbef = web.DataReader('GLW', 'yahoo', start, crashstart)
GLWbef['avgreturnbefore'] = GLWbef['Close'].pct_change(1)
GLWbef['Cumulative Return Before Crash'] = (1 + GLWbef['avgreturnbefore']).cumprod()

#period of crash
GLWdur = web.DataReader('GLW', 'yahoo', crashstart, crashend)
GLWdur['avgreturnduring'] = GLWdur['Close'].pct_change(1)
GLWdur['Cumulative Return During Crash'] = (1 + GLWdur['avgreturnduring']).cumprod()

#period after crash
GLWaft = web.DataReader('GLW', 'yahoo', crashend, end)
GLWaft['avgreturnafter'] = GLWaft['Close'].pct_change(1)
GLWaft['Cumulative Return After Crash'] = (1 + GLWaft['avgreturnafter']).cumprod()
In [520]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DXC = web.DataReader('DXC', 'yahoo', start, end)
DXC['returns'] = DXC['Close'].pct_change(1)
DXC['Cumulative Return'] = (1 + DXC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DXCmean = DXC['returns'].mean()
DXCvar = DXC['returns'].var()
DXCkurt = DXC['returns'].kurt()
DXCskew = DXC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DXCbef = web.DataReader('DXC', 'yahoo', start, crashstart)
DXCbef['avgreturnbefore'] = DXCbef['Close'].pct_change(1)
DXCbef['Cumulative Return Before Crash'] = (1 + DXCbef['avgreturnbefore']).cumprod()

#period of crash
DXCdur = web.DataReader('DXC', 'yahoo', crashstart, crashend)
DXCdur['avgreturnduring'] = DXCdur['Close'].pct_change(1)
DXCdur['Cumulative Return During Crash'] = (1 + DXCdur['avgreturnduring']).cumprod()

#period after crash
DXCaft = web.DataReader('DXC', 'yahoo', crashend, end)
DXCaft['avgreturnafter'] = DXCaft['Close'].pct_change(1)
DXCaft['Cumulative Return After Crash'] = (1 + DXCaft['avgreturnafter']).cumprod()
In [521]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FFIV = web.DataReader('FFIV', 'yahoo', start, end)
FFIV['returns'] = FFIV['Close'].pct_change(1)
FFIV['Cumulative Return'] = (1 + FFIV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FFIVmean = FFIV['returns'].mean()
FFIVvar = FFIV['returns'].var()
FFIVkurt = FFIV['returns'].kurt()
FFIVskew = FFIV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FFIVbef = web.DataReader('FFIV', 'yahoo', start, crashstart)
FFIVbef['avgreturnbefore'] = FFIVbef['Close'].pct_change(1)
FFIVbef['Cumulative Return Before Crash'] = (1 + FFIVbef['avgreturnbefore']).cumprod()

#period of crash
FFIVdur = web.DataReader('FFIV', 'yahoo', crashstart, crashend)
FFIVdur['avgreturnduring'] = FFIVdur['Close'].pct_change(1)
FFIVdur['Cumulative Return During Crash'] = (1 + FFIVdur['avgreturnduring']).cumprod()

#period after crash
FFIVaft = web.DataReader('FFIV', 'yahoo', crashend, end)
FFIVaft['avgreturnafter'] = FFIVaft['Close'].pct_change(1)
FFIVaft['Cumulative Return After Crash'] = (1 + FFIVaft['avgreturnafter']).cumprod()
In [522]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FIS = web.DataReader('FIS', 'yahoo', start, end)
FIS['returns'] = FIS['Close'].pct_change(1)
FIS['Cumulative Return'] = (1 + FIS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FISmean = FIS['returns'].mean()
FISvar = FIS['returns'].var()
FISkurt = FIS['returns'].kurt()
FISskew = FIS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FISbef = web.DataReader('FIS', 'yahoo', start, crashstart)
FISbef['avgreturnbefore'] = FISbef['Close'].pct_change(1)
FISbef['Cumulative Return Before Crash'] = (1 + FISbef['avgreturnbefore']).cumprod()

#period of crash
FISdur = web.DataReader('FIS', 'yahoo', crashstart, crashend)
FISdur['avgreturnduring'] = FISdur['Close'].pct_change(1)
FISdur['Cumulative Return During Crash'] = (1 + FISdur['avgreturnduring']).cumprod()

#period after crash
FISaft = web.DataReader('FIS', 'yahoo', crashend, end)
FISaft['avgreturnafter'] = FISaft['Close'].pct_change(1)
FISaft['Cumulative Return After Crash'] = (1 + FISaft['avgreturnafter']).cumprod()
In [523]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FISV = web.DataReader('FISV', 'yahoo', start, end)
FISV['returns'] = FISV['Close'].pct_change(1)
FISV['Cumulative Return'] = (1 + FISV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FISVmean = FISV['returns'].mean()
FISVvar = FISV['returns'].var()
FISVkurt = FISV['returns'].kurt()
FISVskew = FISV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FISVbef = web.DataReader('FISV', 'yahoo', start, crashstart)
FISVbef['avgreturnbefore'] = FISVbef['Close'].pct_change(1)
FISVbef['Cumulative Return Before Crash'] = (1 + FISVbef['avgreturnbefore']).cumprod()

#period of crash
FISVdur = web.DataReader('FISV', 'yahoo', crashstart, crashend)
FISVdur['avgreturnduring'] = FISVdur['Close'].pct_change(1)
FISVdur['Cumulative Return During Crash'] = (1 + FISVdur['avgreturnduring']).cumprod()

#period after crash
FISVaft = web.DataReader('FISV', 'yahoo', crashend, end)
FISVaft['avgreturnafter'] = FISVaft['Close'].pct_change(1)
FISVaft['Cumulative Return After Crash'] = (1 + FISVaft['avgreturnafter']).cumprod()
In [529]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FLT = web.DataReader('FLT', 'yahoo', start, end)
FLT['returns'] = FLT['Close'].pct_change(1)
FLT['Cumulative Return'] = (1 + FLT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FLTmean = FLT['returns'].mean()
FLTvar = FLT['returns'].var()
FLTkurt = FLT['returns'].kurt()
FLTskew = FLT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FLTbef = web.DataReader('FLT', 'yahoo', start, crashstart)
FLTbef['avgreturnbefore'] = FLTbef['Close'].pct_change(1)
FLTbef['Cumulative Return Before Crash'] = (1 + FLTbef['avgreturnbefore']).cumprod()

#period of crash
FLTdur = web.DataReader('FLT', 'yahoo', crashstart, crashend)
FLTdur['avgreturnduring'] = FLTdur['Close'].pct_change(1)
FLTdur['Cumulative Return During Crash'] = (1 + FLTdur['avgreturnduring']).cumprod()

#period after crash
FLTaft = web.DataReader('FLT', 'yahoo', crashend, end)
FLTaft['avgreturnafter'] = FLTaft['Close'].pct_change(1)
FLTaft['Cumulative Return After Crash'] = (1 + FLTaft['avgreturnafter']).cumprod()
In [530]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FLIR = web.DataReader('FLIR', 'yahoo', start, end)
FLIR['returns'] = FLIR['Close'].pct_change(1)
FLIR['Cumulative Return'] = (1 + FLIR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FLIRmean = FLIR['returns'].mean()
FLIRvar = FLIR['returns'].var()
FLIRkurt = FLIR['returns'].kurt()
FLIRskew = FLIR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FLIRbef = web.DataReader('FLIR', 'yahoo', start, crashstart)
FLIRbef['avgreturnbefore'] = FLIRbef['Close'].pct_change(1)
FLIRbef['Cumulative Return Before Crash'] = (1 + FLIRbef['avgreturnbefore']).cumprod()

#period of crash
FLIRdur = web.DataReader('FLIR', 'yahoo', crashstart, crashend)
FLIRdur['avgreturnduring'] = FLIRdur['Close'].pct_change(1)
FLIRdur['Cumulative Return During Crash'] = (1 + FLIRdur['avgreturnduring']).cumprod()

#period after crash
FLIRaft = web.DataReader('FLIR', 'yahoo', crashend, end)
FLIRaft['avgreturnafter'] = FLIRaft['Close'].pct_change(1)
FLIRaft['Cumulative Return After Crash'] = (1 + FLIRaft['avgreturnafter']).cumprod()
In [531]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FTNT = web.DataReader('FTNT', 'yahoo', start, end)
FTNT['returns'] = FTNT['Close'].pct_change(1)
FTNT['Cumulative Return'] = (1 + FTNT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FTNTmean = FTNT['returns'].mean()
FTNTvar = FTNT['returns'].var()
FTNTkurt = FTNT['returns'].kurt()
FTNTskew = FTNT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FTNTbef = web.DataReader('FTNT', 'yahoo', start, crashstart)
FTNTbef['avgreturnbefore'] = FTNTbef['Close'].pct_change(1)
FTNTbef['Cumulative Return Before Crash'] = (1 + FTNTbef['avgreturnbefore']).cumprod()

#period of crash
FTNTdur = web.DataReader('FTNT', 'yahoo', crashstart, crashend)
FTNTdur['avgreturnduring'] = FTNTdur['Close'].pct_change(1)
FTNTdur['Cumulative Return During Crash'] = (1 + FTNTdur['avgreturnduring']).cumprod()

#period after crash
FTNTaft = web.DataReader('FTNT', 'yahoo', crashend, end)
FTNTaft['avgreturnafter'] = FTNTaft['Close'].pct_change(1)
FTNTaft['Cumulative Return After Crash'] = (1 + FTNTaft['avgreturnafter']).cumprod()
In [532]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IT = web.DataReader('IT', 'yahoo', start, end)
IT['returns'] = IT['Close'].pct_change(1)
IT['Cumulative Return'] = (1 + IT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ITmean = IT['returns'].mean()
ITvar = IT['returns'].var()
ITkurt = IT['returns'].kurt()
ITskew = IT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ITbef = web.DataReader('IT', 'yahoo', start, crashstart)
ITbef['avgreturnbefore'] = ITbef['Close'].pct_change(1)
ITbef['Cumulative Return Before Crash'] = (1 + ITbef['avgreturnbefore']).cumprod()

#period of crash
ITdur = web.DataReader('IT', 'yahoo', crashstart, crashend)
ITdur['avgreturnduring'] = ITdur['Close'].pct_change(1)
ITdur['Cumulative Return During Crash'] = (1 + ITdur['avgreturnduring']).cumprod()

#period after crash
ITaft = web.DataReader('IT', 'yahoo', crashend, end)
ITaft['avgreturnafter'] = ITaft['Close'].pct_change(1)
ITaft['Cumulative Return After Crash'] = (1 + ITaft['avgreturnafter']).cumprod()
In [533]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GPN = web.DataReader('GPN', 'yahoo', start, end)
GPN['returns'] = GPN['Close'].pct_change(1)
GPN['Cumulative Return'] = (1 + GPN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GPNmean = GPN['returns'].mean()
GPNvar = GPN['returns'].var()
GPNkurt = GPN['returns'].kurt()
GPNskew = GPN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GPNbef = web.DataReader('GPN', 'yahoo', start, crashstart)
GPNbef['avgreturnbefore'] = GPNbef['Close'].pct_change(1)
GPNbef['Cumulative Return Before Crash'] = (1 + GPNbef['avgreturnbefore']).cumprod()

#period of crash
GPNdur = web.DataReader('GPN', 'yahoo', crashstart, crashend)
GPNdur['avgreturnduring'] = GPNdur['Close'].pct_change(1)
GPNdur['Cumulative Return During Crash'] = (1 + GPNdur['avgreturnduring']).cumprod()

#period after crash
GPNaft = web.DataReader('GPN', 'yahoo', crashend, end)
GPNaft['avgreturnafter'] = GPNaft['Close'].pct_change(1)
GPNaft['Cumulative Return After Crash'] = (1 + GPNaft['avgreturnafter']).cumprod()
In [534]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HPE = web.DataReader('HPE', 'yahoo', start, end)
HPE['returns'] = HPE['Close'].pct_change(1)
HPE['Cumulative Return'] = (1 + HPE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HPEmean = HPE['returns'].mean()
HPEvar = HPE['returns'].var()
HPEkurt = HPE['returns'].kurt()
HPEskew = HPE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HPEbef = web.DataReader('HPE', 'yahoo', start, crashstart)
HPEbef['avgreturnbefore'] = HPEbef['Close'].pct_change(1)
HPEbef['Cumulative Return Before Crash'] = (1 + HPEbef['avgreturnbefore']).cumprod()

#period of crash
HPEdur = web.DataReader('HPE', 'yahoo', crashstart, crashend)
HPEdur['avgreturnduring'] = HPEdur['Close'].pct_change(1)
HPEdur['Cumulative Return During Crash'] = (1 + HPEdur['avgreturnduring']).cumprod()

#period after crash
HPEaft = web.DataReader('HPE', 'yahoo', crashend, end)
HPEaft['avgreturnafter'] = HPEaft['Close'].pct_change(1)
HPEaft['Cumulative Return After Crash'] = (1 + HPEaft['avgreturnafter']).cumprod()
In [535]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HPQ = web.DataReader('HPQ', 'yahoo', start, end)
HPQ['returns'] = HPQ['Close'].pct_change(1)
HPQ['Cumulative Return'] = (1 + HPQ['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HPQmean = HPQ['returns'].mean()
HPQvar = HPQ['returns'].var()
HPQkurt = HPQ['returns'].kurt()
HPQskew = HPQ['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HPQbef = web.DataReader('HPQ', 'yahoo', start, crashstart)
HPQbef['avgreturnbefore'] = HPQbef['Close'].pct_change(1)
HPQbef['Cumulative Return Before Crash'] = (1 + HPQbef['avgreturnbefore']).cumprod()

#period of crash
HPQdur = web.DataReader('HPQ', 'yahoo', crashstart, crashend)
HPQdur['avgreturnduring'] = HPQdur['Close'].pct_change(1)
HPQdur['Cumulative Return During Crash'] = (1 + HPQdur['avgreturnduring']).cumprod()

#period after crash
HPQaft = web.DataReader('HPQ', 'yahoo', crashend, end)
HPQaft['avgreturnafter'] = HPQaft['Close'].pct_change(1)
HPQaft['Cumulative Return After Crash'] = (1 + HPQaft['avgreturnafter']).cumprod()
In [536]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
INTC = web.DataReader('INTC', 'yahoo', start, end)
INTC['returns'] = INTC['Close'].pct_change(1)
INTC['Cumulative Return'] = (1 + INTC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
INTCmean = INTC['returns'].mean()
INTCvar = INTC['returns'].var()
INTCkurt = INTC['returns'].kurt()
INTCskew = INTC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
INTCbef = web.DataReader('INTC', 'yahoo', start, crashstart)
INTCbef['avgreturnbefore'] = INTCbef['Close'].pct_change(1)
INTCbef['Cumulative Return Before Crash'] = (1 + INTCbef['avgreturnbefore']).cumprod()

#period of crash
INTCdur = web.DataReader('INTC', 'yahoo', crashstart, crashend)
INTCdur['avgreturnduring'] = INTCdur['Close'].pct_change(1)
INTCdur['Cumulative Return During Crash'] = (1 + INTCdur['avgreturnduring']).cumprod()

#period after crash
INTCaft = web.DataReader('INTC', 'yahoo', crashend, end)
INTCaft['avgreturnafter'] = INTCaft['Close'].pct_change(1)
INTCaft['Cumulative Return After Crash'] = (1 + INTCaft['avgreturnafter']).cumprod()
In [537]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IBM = web.DataReader('IBM', 'yahoo', start, end)
IBM['returns'] = IBM['Close'].pct_change(1)
IBM['Cumulative Return'] = (1 + IBM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IBMmean = IBM['returns'].mean()
IBMvar = IBM['returns'].var()
IBMkurt = IBM['returns'].kurt()
IBMskew = IBM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IBMbef = web.DataReader('IBM', 'yahoo', start, crashstart)
IBMbef['avgreturnbefore'] = IBMbef['Close'].pct_change(1)
IBMbef['Cumulative Return Before Crash'] = (1 + IBMbef['avgreturnbefore']).cumprod()

#period of crash
IBMdur = web.DataReader('IBM', 'yahoo', crashstart, crashend)
IBMdur['avgreturnduring'] = IBMdur['Close'].pct_change(1)
IBMdur['Cumulative Return During Crash'] = (1 + IBMdur['avgreturnduring']).cumprod()

#period after crash
IBMaft = web.DataReader('IBM', 'yahoo', crashend, end)
IBMaft['avgreturnafter'] = IBMaft['Close'].pct_change(1)
IBMaft['Cumulative Return After Crash'] = (1 + IBMaft['avgreturnafter']).cumprod()
In [538]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
INTU = web.DataReader('INTU', 'yahoo', start, end)
INTU['returns'] = INTU['Close'].pct_change(1)
INTU['Cumulative Return'] = (1 + INTU['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
INTUmean = INTU['returns'].mean()
INTUvar = INTU['returns'].var()
INTUkurt = INTU['returns'].kurt()
INTUskew = INTU['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
INTUbef = web.DataReader('INTU', 'yahoo', start, crashstart)
INTUbef['avgreturnbefore'] = INTUbef['Close'].pct_change(1)
INTUbef['Cumulative Return Before Crash'] = (1 + INTUbef['avgreturnbefore']).cumprod()

#period of crash
INTUdur = web.DataReader('INTU', 'yahoo', crashstart, crashend)
INTUdur['avgreturnduring'] = INTUdur['Close'].pct_change(1)
INTUdur['Cumulative Return During Crash'] = (1 + INTUdur['avgreturnduring']).cumprod()

#period after crash
INTUaft = web.DataReader('INTU', 'yahoo', crashend, end)
INTUaft['avgreturnafter'] = INTUaft['Close'].pct_change(1)
INTUaft['Cumulative Return After Crash'] = (1 + INTUaft['avgreturnafter']).cumprod()
In [539]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IPGP = web.DataReader('IPGP', 'yahoo', start, end)
IPGP['returns'] = IPGP['Close'].pct_change(1)
IPGP['Cumulative Return'] = (1 + IPGP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IPGPmean = IPGP['returns'].mean()
IPGPvar = IPGP['returns'].var()
IPGPkurt = IPGP['returns'].kurt()
IPGPskew = IPGP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IPGPbef = web.DataReader('IPGP', 'yahoo', start, crashstart)
IPGPbef['avgreturnbefore'] = IPGPbef['Close'].pct_change(1)
IPGPbef['Cumulative Return Before Crash'] = (1 + IPGPbef['avgreturnbefore']).cumprod()

#period of crash
IPGPdur = web.DataReader('IPGP', 'yahoo', crashstart, crashend)
IPGPdur['avgreturnduring'] = IPGPdur['Close'].pct_change(1)
IPGPdur['Cumulative Return During Crash'] = (1 + IPGPdur['avgreturnduring']).cumprod()

#period after crash
IPGPaft = web.DataReader('IPGP', 'yahoo', crashend, end)
IPGPaft['avgreturnafter'] = IPGPaft['Close'].pct_change(1)
IPGPaft['Cumulative Return After Crash'] = (1 + IPGPaft['avgreturnafter']).cumprod()
In [540]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
JKHY = web.DataReader('JKHY', 'yahoo', start, end)
JKHY['returns'] = JKHY['Close'].pct_change(1)
JKHY['Cumulative Return'] = (1 + JKHY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
JKHYmean = JKHY['returns'].mean()
JKHYvar = JKHY['returns'].var()
JKHYkurt = JKHY['returns'].kurt()
JKHYskew = JKHY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
JKHYbef = web.DataReader('JKHY', 'yahoo', start, crashstart)
JKHYbef['avgreturnbefore'] = JKHYbef['Close'].pct_change(1)
JKHYbef['Cumulative Return Before Crash'] = (1 + JKHYbef['avgreturnbefore']).cumprod()

#period of crash
JKHYdur = web.DataReader('JKHY', 'yahoo', crashstart, crashend)
JKHYdur['avgreturnduring'] = JKHYdur['Close'].pct_change(1)
JKHYdur['Cumulative Return During Crash'] = (1 + JKHYdur['avgreturnduring']).cumprod()

#period after crash
JKHYaft = web.DataReader('JKHY', 'yahoo', crashend, end)
JKHYaft['avgreturnafter'] = JKHYaft['Close'].pct_change(1)
JKHYaft['Cumulative Return After Crash'] = (1 + JKHYaft['avgreturnafter']).cumprod()
In [541]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
JNPR = web.DataReader('JNPR', 'yahoo', start, end)
JNPR['returns'] = JNPR['Close'].pct_change(1)
JNPR['Cumulative Return'] = (1 + JNPR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
JNPRmean = JNPR['returns'].mean()
JNPRvar = JNPR['returns'].var()
JNPRkurt = JNPR['returns'].kurt()
JNPRskew = JNPR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
JNPRbef = web.DataReader('JNPR', 'yahoo', start, crashstart)
JNPRbef['avgreturnbefore'] = JNPRbef['Close'].pct_change(1)
JNPRbef['Cumulative Return Before Crash'] = (1 + JNPRbef['avgreturnbefore']).cumprod()

#period of crash
JNPRdur = web.DataReader('JNPR', 'yahoo', crashstart, crashend)
JNPRdur['avgreturnduring'] = JNPRdur['Close'].pct_change(1)
JNPRdur['Cumulative Return During Crash'] = (1 + JNPRdur['avgreturnduring']).cumprod()

#period after crash
JNPRaft = web.DataReader('JNPR', 'yahoo', crashend, end)
JNPRaft['avgreturnafter'] = JNPRaft['Close'].pct_change(1)
JNPRaft['Cumulative Return After Crash'] = (1 + JNPRaft['avgreturnafter']).cumprod()
In [542]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KEYS = web.DataReader('KEYS', 'yahoo', start, end)
KEYS['returns'] = KEYS['Close'].pct_change(1)
KEYS['Cumulative Return'] = (1 + KEYS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KEYSmean = KEYS['returns'].mean()
KEYSvar = KEYS['returns'].var()
KEYSkurt = KEYS['returns'].kurt()
KEYSskew = KEYS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KEYSbef = web.DataReader('KEYS', 'yahoo', start, crashstart)
KEYSbef['avgreturnbefore'] = KEYSbef['Close'].pct_change(1)
KEYSbef['Cumulative Return Before Crash'] = (1 + KEYSbef['avgreturnbefore']).cumprod()

#period of crash
KEYSdur = web.DataReader('KEYS', 'yahoo', crashstart, crashend)
KEYSdur['avgreturnduring'] = KEYSdur['Close'].pct_change(1)
KEYSdur['Cumulative Return During Crash'] = (1 + KEYSdur['avgreturnduring']).cumprod()

#period after crash
KEYSaft = web.DataReader('KEYS', 'yahoo', crashend, end)
KEYSaft['avgreturnafter'] = KEYSaft['Close'].pct_change(1)
KEYSaft['Cumulative Return After Crash'] = (1 + KEYSaft['avgreturnafter']).cumprod()
In [543]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KLAC = web.DataReader('KLAC', 'yahoo', start, end)
KLAC['returns'] = KLAC['Close'].pct_change(1)
KLAC['Cumulative Return'] = (1 + KLAC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KLACmean = KLAC['returns'].mean()
KLACvar = KLAC['returns'].var()
KLACkurt = KLAC['returns'].kurt()
KLACskew = KLAC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KLACbef = web.DataReader('KLAC', 'yahoo', start, crashstart)
KLACbef['avgreturnbefore'] = KLACbef['Close'].pct_change(1)
KLACbef['Cumulative Return Before Crash'] = (1 + KLACbef['avgreturnbefore']).cumprod()

#period of crash
KLACdur = web.DataReader('KLAC', 'yahoo', crashstart, crashend)
KLACdur['avgreturnduring'] = KLACdur['Close'].pct_change(1)
KLACdur['Cumulative Return During Crash'] = (1 + KLACdur['avgreturnduring']).cumprod()

#period after crash
KLACaft = web.DataReader('KLAC', 'yahoo', crashend, end)
KLACaft['avgreturnafter'] = KLACaft['Close'].pct_change(1)
KLACaft['Cumulative Return After Crash'] = (1 + KLACaft['avgreturnafter']).cumprod()
In [544]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LRCX = web.DataReader('LRCX', 'yahoo', start, end)
LRCX['returns'] = LRCX['Close'].pct_change(1)
LRCX['Cumulative Return'] = (1 + LRCX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LRCXmean = LRCX['returns'].mean()
LRCXvar = LRCX['returns'].var()
LRCXkurt = LRCX['returns'].kurt()
LRCXskew = LRCX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LRCXbef = web.DataReader('LRCX', 'yahoo', start, crashstart)
LRCXbef['avgreturnbefore'] = LRCXbef['Close'].pct_change(1)
LRCXbef['Cumulative Return Before Crash'] = (1 + LRCXbef['avgreturnbefore']).cumprod()

#period of crash
LRCXdur = web.DataReader('LRCX', 'yahoo', crashstart, crashend)
LRCXdur['avgreturnduring'] = LRCXdur['Close'].pct_change(1)
LRCXdur['Cumulative Return During Crash'] = (1 + LRCXdur['avgreturnduring']).cumprod()

#period after crash
LRCXaft = web.DataReader('LRCX', 'yahoo', crashend, end)
LRCXaft['avgreturnafter'] = LRCXaft['Close'].pct_change(1)
LRCXaft['Cumulative Return After Crash'] = (1 + LRCXaft['avgreturnafter']).cumprod()
In [545]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LDOS = web.DataReader('LDOS', 'yahoo', start, end)
LDOS['returns'] = LDOS['Close'].pct_change(1)
LDOS['Cumulative Return'] = (1 + LDOS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LDOSmean = LDOS['returns'].mean()
LDOSvar = LDOS['returns'].var()
LDOSkurt = LDOS['returns'].kurt()
LDOSskew = LDOS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LDOSbef = web.DataReader('LDOS', 'yahoo', start, crashstart)
LDOSbef['avgreturnbefore'] = LDOSbef['Close'].pct_change(1)
LDOSbef['Cumulative Return Before Crash'] = (1 + LDOSbef['avgreturnbefore']).cumprod()

#period of crash
LDOSdur = web.DataReader('LDOS', 'yahoo', crashstart, crashend)
LDOSdur['avgreturnduring'] = LDOSdur['Close'].pct_change(1)
LDOSdur['Cumulative Return During Crash'] = (1 + LDOSdur['avgreturnduring']).cumprod()

#period after crash
LDOSaft = web.DataReader('LDOS', 'yahoo', crashend, end)
LDOSaft['avgreturnafter'] = LDOSaft['Close'].pct_change(1)
LDOSaft['Cumulative Return After Crash'] = (1 + LDOSaft['avgreturnafter']).cumprod()
In [546]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MA = web.DataReader('MA', 'yahoo', start, end)
MA['returns'] = MA['Close'].pct_change(1)
MA['Cumulative Return'] = (1 + MA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MAmean = MA['returns'].mean()
MAvar = MA['returns'].var()
MAkurt = MA['returns'].kurt()
MAskew = MA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MAbef = web.DataReader('MA', 'yahoo', start, crashstart)
MAbef['avgreturnbefore'] = MAbef['Close'].pct_change(1)
MAbef['Cumulative Return Before Crash'] = (1 + MAbef['avgreturnbefore']).cumprod()

#period of crash
MAdur = web.DataReader('MA', 'yahoo', crashstart, crashend)
MAdur['avgreturnduring'] = MAdur['Close'].pct_change(1)
MAdur['Cumulative Return During Crash'] = (1 + MAdur['avgreturnduring']).cumprod()

#period after crash
MAaft = web.DataReader('MA', 'yahoo', crashend, end)
MAaft['avgreturnafter'] = MAaft['Close'].pct_change(1)
MAaft['Cumulative Return After Crash'] = (1 + MAaft['avgreturnafter']).cumprod()
In [547]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MXIM = web.DataReader('MXIM', 'yahoo', start, end)
MXIM['returns'] = MXIM['Close'].pct_change(1)
MXIM['Cumulative Return'] = (1 + MXIM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MXIMmean = MXIM['returns'].mean()
MXIMvar = MXIM['returns'].var()
MXIMkurt = MXIM['returns'].kurt()
MXIMskew = MXIM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MXIMbef = web.DataReader('MXIM', 'yahoo', start, crashstart)
MXIMbef['avgreturnbefore'] = MXIMbef['Close'].pct_change(1)
MXIMbef['Cumulative Return Before Crash'] = (1 + MXIMbef['avgreturnbefore']).cumprod()

#period of crash
MXIMdur = web.DataReader('MXIM', 'yahoo', crashstart, crashend)
MXIMdur['avgreturnduring'] = MXIMdur['Close'].pct_change(1)
MXIMdur['Cumulative Return During Crash'] = (1 + MXIMdur['avgreturnduring']).cumprod()

#period after crash
MXIMaft = web.DataReader('MXIM', 'yahoo', crashend, end)
MXIMaft['avgreturnafter'] = MXIMaft['Close'].pct_change(1)
MXIMaft['Cumulative Return After Crash'] = (1 + MXIMaft['avgreturnafter']).cumprod()
In [548]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MCHP = web.DataReader('MCHP', 'yahoo', start, end)
MCHP['returns'] = MCHP['Close'].pct_change(1)
MCHP['Cumulative Return'] = (1 + MCHP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MCHPmean = MCHP['returns'].mean()
MCHPvar = MCHP['returns'].var()
MCHPkurt = MCHP['returns'].kurt()
MCHPskew = MCHP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MCHPbef = web.DataReader('MCHP', 'yahoo', start, crashstart)
MCHPbef['avgreturnbefore'] = MCHPbef['Close'].pct_change(1)
MCHPbef['Cumulative Return Before Crash'] = (1 + MCHPbef['avgreturnbefore']).cumprod()

#period of crash
MCHPdur = web.DataReader('MCHP', 'yahoo', crashstart, crashend)
MCHPdur['avgreturnduring'] = MCHPdur['Close'].pct_change(1)
MCHPdur['Cumulative Return During Crash'] = (1 + MCHPdur['avgreturnduring']).cumprod()

#period after crash
MCHPaft = web.DataReader('MCHP', 'yahoo', crashend, end)
MCHPaft['avgreturnafter'] = MCHPaft['Close'].pct_change(1)
MCHPaft['Cumulative Return After Crash'] = (1 + MCHPaft['avgreturnafter']).cumprod()
In [549]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MU = web.DataReader('MU', 'yahoo', start, end)
MU['returns'] = MU['Close'].pct_change(1)
MU['Cumulative Return'] = (1 + MU['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MUmean = MU['returns'].mean()
MUvar = MU['returns'].var()
MUkurt = MU['returns'].kurt()
MUskew = MU['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MUbef = web.DataReader('MU', 'yahoo', start, crashstart)
MUbef['avgreturnbefore'] = MUbef['Close'].pct_change(1)
MUbef['Cumulative Return Before Crash'] = (1 + MUbef['avgreturnbefore']).cumprod()

#period of crash
MUdur = web.DataReader('MU', 'yahoo', crashstart, crashend)
MUdur['avgreturnduring'] = MUdur['Close'].pct_change(1)
MUdur['Cumulative Return During Crash'] = (1 + MUdur['avgreturnduring']).cumprod()

#period after crash
MUaft = web.DataReader('MU', 'yahoo', crashend, end)
MUaft['avgreturnafter'] = MUaft['Close'].pct_change(1)
MUaft['Cumulative Return After Crash'] = (1 + MUaft['avgreturnafter']).cumprod()
In [550]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MSFT = web.DataReader('MSFT', 'yahoo', start, end)
MSFT['returns'] = MSFT['Close'].pct_change(1)
MSFT['Cumulative Return'] = (1 + MSFT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MSFTmean = MSFT['returns'].mean()
MSFTvar = MSFT['returns'].var()
MSFTkurt = MSFT['returns'].kurt()
MSFTskew = MSFT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MSFTbef = web.DataReader('MSFT', 'yahoo', start, crashstart)
MSFTbef['avgreturnbefore'] = MSFTbef['Close'].pct_change(1)
MSFTbef['Cumulative Return Before Crash'] = (1 + MSFTbef['avgreturnbefore']).cumprod()

#period of crash
MSFTdur = web.DataReader('MSFT', 'yahoo', crashstart, crashend)
MSFTdur['avgreturnduring'] = MSFTdur['Close'].pct_change(1)
MSFTdur['Cumulative Return During Crash'] = (1 + MSFTdur['avgreturnduring']).cumprod()

#period after crash
MSFTaft = web.DataReader('MSFT', 'yahoo', crashend, end)
MSFTaft['avgreturnafter'] = MSFTaft['Close'].pct_change(1)
MSFTaft['Cumulative Return After Crash'] = (1 + MSFTaft['avgreturnafter']).cumprod()
In [551]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MSI = web.DataReader('MSI', 'yahoo', start, end)
MSI['returns'] = MSI['Close'].pct_change(1)
MSI['Cumulative Return'] = (1 + MSI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MSImean = MSI['returns'].mean()
MSIvar = MSI['returns'].var()
MSIkurt = MSI['returns'].kurt()
MSIskew = MSI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MSIbef = web.DataReader('MSI', 'yahoo', start, crashstart)
MSIbef['avgreturnbefore'] = MSIbef['Close'].pct_change(1)
MSIbef['Cumulative Return Before Crash'] = (1 + MSIbef['avgreturnbefore']).cumprod()

#period of crash
MSIdur = web.DataReader('MSI', 'yahoo', crashstart, crashend)
MSIdur['avgreturnduring'] = MSIdur['Close'].pct_change(1)
MSIdur['Cumulative Return During Crash'] = (1 + MSIdur['avgreturnduring']).cumprod()

#period after crash
MSIaft = web.DataReader('MSI', 'yahoo', crashend, end)
MSIaft['avgreturnafter'] = MSIaft['Close'].pct_change(1)
MSIaft['Cumulative Return After Crash'] = (1 + MSIaft['avgreturnafter']).cumprod()
In [552]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NTAP = web.DataReader('NTAP', 'yahoo', start, end)
NTAP['returns'] = NTAP['Close'].pct_change(1)
NTAP['Cumulative Return'] = (1 + NTAP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NTAPmean = NTAP['returns'].mean()
NTAPvar = NTAP['returns'].var()
NTAPkurt = NTAP['returns'].kurt()
NTAPskew = NTAP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NTAPbef = web.DataReader('NTAP', 'yahoo', start, crashstart)
NTAPbef['avgreturnbefore'] = NTAPbef['Close'].pct_change(1)
NTAPbef['Cumulative Return Before Crash'] = (1 + NTAPbef['avgreturnbefore']).cumprod()

#period of crash
NTAPdur = web.DataReader('NTAP', 'yahoo', crashstart, crashend)
NTAPdur['avgreturnduring'] = NTAPdur['Close'].pct_change(1)
NTAPdur['Cumulative Return During Crash'] = (1 + NTAPdur['avgreturnduring']).cumprod()

#period after crash
NTAPaft = web.DataReader('NTAP', 'yahoo', crashend, end)
NTAPaft['avgreturnafter'] = NTAPaft['Close'].pct_change(1)
NTAPaft['Cumulative Return After Crash'] = (1 + NTAPaft['avgreturnafter']).cumprod()
In [553]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NLOK = web.DataReader('NLOK', 'yahoo', start, end)
NLOK['returns'] = NLOK['Close'].pct_change(1)
NLOK['Cumulative Return'] = (1 + NLOK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NLOKmean = NLOK['returns'].mean()
NLOKvar = NLOK['returns'].var()
NLOKkurt = NLOK['returns'].kurt()
NLOKskew = NLOK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NLOKbef = web.DataReader('NLOK', 'yahoo', start, crashstart)
NLOKbef['avgreturnbefore'] = NLOKbef['Close'].pct_change(1)
NLOKbef['Cumulative Return Before Crash'] = (1 + NLOKbef['avgreturnbefore']).cumprod()

#period of crash
NLOKdur = web.DataReader('NLOK', 'yahoo', crashstart, crashend)
NLOKdur['avgreturnduring'] = NLOKdur['Close'].pct_change(1)
NLOKdur['Cumulative Return During Crash'] = (1 + NLOKdur['avgreturnduring']).cumprod()

#period after crash
NLOKaft = web.DataReader('NLOK', 'yahoo', crashend, end)
NLOKaft['avgreturnafter'] = NLOKaft['Close'].pct_change(1)
NLOKaft['Cumulative Return After Crash'] = (1 + NLOKaft['avgreturnafter']).cumprod()
In [554]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NVDA = web.DataReader('NVDA', 'yahoo', start, end)
NVDA['returns'] = NVDA['Close'].pct_change(1)
NVDA['Cumulative Return'] = (1 + NVDA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NVDAmean = NVDA['returns'].mean()
NVDAvar = NVDA['returns'].var()
NVDAkurt = NVDA['returns'].kurt()
NVDAskew = NVDA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NVDAbef = web.DataReader('NVDA', 'yahoo', start, crashstart)
NVDAbef['avgreturnbefore'] = NVDAbef['Close'].pct_change(1)
NVDAbef['Cumulative Return Before Crash'] = (1 + NVDAbef['avgreturnbefore']).cumprod()

#period of crash
NVDAdur = web.DataReader('NVDA', 'yahoo', crashstart, crashend)
NVDAdur['avgreturnduring'] = NVDAdur['Close'].pct_change(1)
NVDAdur['Cumulative Return During Crash'] = (1 + NVDAdur['avgreturnduring']).cumprod()

#period after crash
NVDAaft = web.DataReader('NVDA', 'yahoo', crashend, end)
NVDAaft['avgreturnafter'] = NVDAaft['Close'].pct_change(1)
NVDAaft['Cumulative Return After Crash'] = (1 + NVDAaft['avgreturnafter']).cumprod()
In [555]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ORCL = web.DataReader('ORCL', 'yahoo', start, end)
ORCL['returns'] = ORCL['Close'].pct_change(1)
ORCL['Cumulative Return'] = (1 + ORCL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ORCLmean = ORCL['returns'].mean()
ORCLvar = ORCL['returns'].var()
ORCLkurt = ORCL['returns'].kurt()
ORCLskew = ORCL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ORCLbef = web.DataReader('ORCL', 'yahoo', start, crashstart)
ORCLbef['avgreturnbefore'] = ORCLbef['Close'].pct_change(1)
ORCLbef['Cumulative Return Before Crash'] = (1 + ORCLbef['avgreturnbefore']).cumprod()

#period of crash
ORCLdur = web.DataReader('ORCL', 'yahoo', crashstart, crashend)
ORCLdur['avgreturnduring'] = ORCLdur['Close'].pct_change(1)
ORCLdur['Cumulative Return During Crash'] = (1 + ORCLdur['avgreturnduring']).cumprod()

#period after crash
ORCLaft = web.DataReader('ORCL', 'yahoo', crashend, end)
ORCLaft['avgreturnafter'] = ORCLaft['Close'].pct_change(1)
ORCLaft['Cumulative Return After Crash'] = (1 + ORCLaft['avgreturnafter']).cumprod()
In [556]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PAYX = web.DataReader('PAYX', 'yahoo', start, end)
PAYX['returns'] = PAYX['Close'].pct_change(1)
PAYX['Cumulative Return'] = (1 + PAYX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PAYXmean = PAYX['returns'].mean()
PAYXvar = PAYX['returns'].var()
PAYXkurt = PAYX['returns'].kurt()
PAYXskew = PAYX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PAYXbef = web.DataReader('PAYX', 'yahoo', start, crashstart)
PAYXbef['avgreturnbefore'] = PAYXbef['Close'].pct_change(1)
PAYXbef['Cumulative Return Before Crash'] = (1 + PAYXbef['avgreturnbefore']).cumprod()

#period of crash
PAYXdur = web.DataReader('PAYX', 'yahoo', crashstart, crashend)
PAYXdur['avgreturnduring'] = PAYXdur['Close'].pct_change(1)
PAYXdur['Cumulative Return During Crash'] = (1 + PAYXdur['avgreturnduring']).cumprod()

#period after crash
PAYXaft = web.DataReader('PAYX', 'yahoo', crashend, end)
PAYXaft['avgreturnafter'] = PAYXaft['Close'].pct_change(1)
PAYXaft['Cumulative Return After Crash'] = (1 + PAYXaft['avgreturnafter']).cumprod()
In [557]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PAYC = web.DataReader('PAYC', 'yahoo', start, end)
PAYC['returns'] = PAYC['Close'].pct_change(1)
PAYC['Cumulative Return'] = (1 + PAYC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PAYCmean = PAYC['returns'].mean()
PAYCvar = PAYC['returns'].var()
PAYCkurt = PAYC['returns'].kurt()
PAYCskew = PAYC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PAYCbef = web.DataReader('PAYC', 'yahoo', start, crashstart)
PAYCbef['avgreturnbefore'] = PAYCbef['Close'].pct_change(1)
PAYCbef['Cumulative Return Before Crash'] = (1 + PAYCbef['avgreturnbefore']).cumprod()

#period of crash
PAYCdur = web.DataReader('PAYC', 'yahoo', crashstart, crashend)
PAYCdur['avgreturnduring'] = PAYCdur['Close'].pct_change(1)
PAYCdur['Cumulative Return During Crash'] = (1 + PAYCdur['avgreturnduring']).cumprod()

#period after crash
PAYCaft = web.DataReader('PAYC', 'yahoo', crashend, end)
PAYCaft['avgreturnafter'] = PAYCaft['Close'].pct_change(1)
PAYCaft['Cumulative Return After Crash'] = (1 + PAYCaft['avgreturnafter']).cumprod()
In [558]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PYPL = web.DataReader('PYPL', 'yahoo', start, end)
PYPL['returns'] = PYPL['Close'].pct_change(1)
PYPL['Cumulative Return'] = (1 + PYPL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PYPLmean = PYPL['returns'].mean()
PYPLvar = PYPL['returns'].var()
PYPLkurt = PYPL['returns'].kurt()
PYPLskew = PYPL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PYPLbef = web.DataReader('PYPL', 'yahoo', start, crashstart)
PYPLbef['avgreturnbefore'] = PYPLbef['Close'].pct_change(1)
PYPLbef['Cumulative Return Before Crash'] = (1 + PYPLbef['avgreturnbefore']).cumprod()

#period of crash
PYPLdur = web.DataReader('PYPL', 'yahoo', crashstart, crashend)
PYPLdur['avgreturnduring'] = PYPLdur['Close'].pct_change(1)
PYPLdur['Cumulative Return During Crash'] = (1 + PYPLdur['avgreturnduring']).cumprod()

#period after crash
PYPLaft = web.DataReader('PYPL', 'yahoo', crashend, end)
PYPLaft['avgreturnafter'] = PYPLaft['Close'].pct_change(1)
PYPLaft['Cumulative Return After Crash'] = (1 + PYPLaft['avgreturnafter']).cumprod()
In [559]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
QRVO = web.DataReader('QRVO', 'yahoo', start, end)
QRVO['returns'] = QRVO['Close'].pct_change(1)
QRVO['Cumulative Return'] = (1 + QRVO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
QRVOmean = QRVO['returns'].mean()
QRVOvar = QRVO['returns'].var()
QRVOkurt = QRVO['returns'].kurt()
QRVOskew = QRVO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
QRVObef = web.DataReader('QRVO', 'yahoo', start, crashstart)
QRVObef['avgreturnbefore'] = QRVObef['Close'].pct_change(1)
QRVObef['Cumulative Return Before Crash'] = (1 + QRVObef['avgreturnbefore']).cumprod()

#period of crash
QRVOdur = web.DataReader('QRVO', 'yahoo', crashstart, crashend)
QRVOdur['avgreturnduring'] = QRVOdur['Close'].pct_change(1)
QRVOdur['Cumulative Return During Crash'] = (1 + QRVOdur['avgreturnduring']).cumprod()

#period after crash
QRVOaft = web.DataReader('QRVO', 'yahoo', crashend, end)
QRVOaft['avgreturnafter'] = QRVOaft['Close'].pct_change(1)
QRVOaft['Cumulative Return After Crash'] = (1 + QRVOaft['avgreturnafter']).cumprod()
In [560]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
QCOM = web.DataReader('QCOM', 'yahoo', start, end)
QCOM['returns'] = QCOM['Close'].pct_change(1)
QCOM['Cumulative Return'] = (1 + QCOM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
QCOMmean = QCOM['returns'].mean()
QCOMvar = QCOM['returns'].var()
QCOMkurt = QCOM['returns'].kurt()
QCOMskew = QCOM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
QCOMbef = web.DataReader('QCOM', 'yahoo', start, crashstart)
QCOMbef['avgreturnbefore'] = QCOMbef['Close'].pct_change(1)
QCOMbef['Cumulative Return Before Crash'] = (1 + QCOMbef['avgreturnbefore']).cumprod()

#period of crash
QCOMdur = web.DataReader('QCOM', 'yahoo', crashstart, crashend)
QCOMdur['avgreturnduring'] = QCOMdur['Close'].pct_change(1)
QCOMdur['Cumulative Return During Crash'] = (1 + QCOMdur['avgreturnduring']).cumprod()

#period after crash
QCOMaft = web.DataReader('QCOM', 'yahoo', crashend, end)
QCOMaft['avgreturnafter'] = QCOMaft['Close'].pct_change(1)
QCOMaft['Cumulative Return After Crash'] = (1 + QCOMaft['avgreturnafter']).cumprod()
In [561]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CRM = web.DataReader('CRM', 'yahoo', start, end)
CRM['returns'] = CRM['Close'].pct_change(1)
CRM['Cumulative Return'] = (1 + CRM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CRMmean = CRM['returns'].mean()
CRMvar = CRM['returns'].var()
CRMkurt = CRM['returns'].kurt()
CRMskew = CRM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CRMbef = web.DataReader('CRM', 'yahoo', start, crashstart)
CRMbef['avgreturnbefore'] = CRMbef['Close'].pct_change(1)
CRMbef['Cumulative Return Before Crash'] = (1 + CRMbef['avgreturnbefore']).cumprod()

#period of crash
CRMdur = web.DataReader('CRM', 'yahoo', crashstart, crashend)
CRMdur['avgreturnduring'] = CRMdur['Close'].pct_change(1)
CRMdur['Cumulative Return During Crash'] = (1 + CRMdur['avgreturnduring']).cumprod()

#period after crash
CRMaft = web.DataReader('CRM', 'yahoo', crashend, end)
CRMaft['avgreturnafter'] = CRMaft['Close'].pct_change(1)
CRMaft['Cumulative Return After Crash'] = (1 + CRMaft['avgreturnafter']).cumprod()
In [562]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
STX = web.DataReader('STX', 'yahoo', start, end)
STX['returns'] = STX['Close'].pct_change(1)
STX['Cumulative Return'] = (1 + STX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
STXmean = STX['returns'].mean()
STXvar = STX['returns'].var()
STXkurt = STX['returns'].kurt()
STXskew = STX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
STXbef = web.DataReader('STX', 'yahoo', start, crashstart)
STXbef['avgreturnbefore'] = STXbef['Close'].pct_change(1)
STXbef['Cumulative Return Before Crash'] = (1 + STXbef['avgreturnbefore']).cumprod()

#period of crash
STXdur = web.DataReader('STX', 'yahoo', crashstart, crashend)
STXdur['avgreturnduring'] = STXdur['Close'].pct_change(1)
STXdur['Cumulative Return During Crash'] = (1 + STXdur['avgreturnduring']).cumprod()

#period after crash
STXaft = web.DataReader('STX', 'yahoo', crashend, end)
STXaft['avgreturnafter'] = STXaft['Close'].pct_change(1)
STXaft['Cumulative Return After Crash'] = (1 + STXaft['avgreturnafter']).cumprod()
In [563]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NOW = web.DataReader('NOW', 'yahoo', start, end)
NOW['returns'] = NOW['Close'].pct_change(1)
NOW['Cumulative Return'] = (1 + NOW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NOWmean = NOW['returns'].mean()
NOWvar = NOW['returns'].var()
NOWkurt = NOW['returns'].kurt()
NOWskew = NOW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NOWbef = web.DataReader('NOW', 'yahoo', start, crashstart)
NOWbef['avgreturnbefore'] = NOWbef['Close'].pct_change(1)
NOWbef['Cumulative Return Before Crash'] = (1 + NOWbef['avgreturnbefore']).cumprod()

#period of crash
NOWdur = web.DataReader('NOW', 'yahoo', crashstart, crashend)
NOWdur['avgreturnduring'] = NOWdur['Close'].pct_change(1)
NOWdur['Cumulative Return During Crash'] = (1 + NOWdur['avgreturnduring']).cumprod()

#period after crash
NOWaft = web.DataReader('NOW', 'yahoo', crashend, end)
NOWaft['avgreturnafter'] = NOWaft['Close'].pct_change(1)
NOWaft['Cumulative Return After Crash'] = (1 + NOWaft['avgreturnafter']).cumprod()
In [564]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SWKS = web.DataReader('SWKS', 'yahoo', start, end)
SWKS['returns'] = SWKS['Close'].pct_change(1)
SWKS['Cumulative Return'] = (1 + SWKS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SWKSmean = SWKS['returns'].mean()
SWKSvar = SWKS['returns'].var()
SWKSkurt = SWKS['returns'].kurt()
SWKSskew = SWKS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SWKSbef = web.DataReader('SWKS', 'yahoo', start, crashstart)
SWKSbef['avgreturnbefore'] = SWKSbef['Close'].pct_change(1)
SWKSbef['Cumulative Return Before Crash'] = (1 + SWKSbef['avgreturnbefore']).cumprod()

#period of crash
SWKSdur = web.DataReader('SWKS', 'yahoo', crashstart, crashend)
SWKSdur['avgreturnduring'] = SWKSdur['Close'].pct_change(1)
SWKSdur['Cumulative Return During Crash'] = (1 + SWKSdur['avgreturnduring']).cumprod()

#period after crash
SWKSaft = web.DataReader('SWKS', 'yahoo', crashend, end)
SWKSaft['avgreturnafter'] = SWKSaft['Close'].pct_change(1)
SWKSaft['Cumulative Return After Crash'] = (1 + SWKSaft['avgreturnafter']).cumprod()
In [565]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SNPS = web.DataReader('SNPS', 'yahoo', start, end)
SNPS['returns'] = SNPS['Close'].pct_change(1)
SNPS['Cumulative Return'] = (1 + SNPS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SNPSmean = SNPS['returns'].mean()
SNPSvar = SNPS['returns'].var()
SNPSkurt = SNPS['returns'].kurt()
SNPSskew = SNPS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SNPSbef = web.DataReader('SNPS', 'yahoo', start, crashstart)
SNPSbef['avgreturnbefore'] = SNPSbef['Close'].pct_change(1)
SNPSbef['Cumulative Return Before Crash'] = (1 + SNPSbef['avgreturnbefore']).cumprod()

#period of crash
SNPSdur = web.DataReader('SNPS', 'yahoo', crashstart, crashend)
SNPSdur['avgreturnduring'] = SNPSdur['Close'].pct_change(1)
SNPSdur['Cumulative Return During Crash'] = (1 + SNPSdur['avgreturnduring']).cumprod()

#period after crash
SNPSaft = web.DataReader('SNPS', 'yahoo', crashend, end)
SNPSaft['avgreturnafter'] = SNPSaft['Close'].pct_change(1)
SNPSaft['Cumulative Return After Crash'] = (1 + SNPSaft['avgreturnafter']).cumprod()
In [566]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TEL = web.DataReader('TEL', 'yahoo', start, end)
TEL['returns'] = TEL['Close'].pct_change(1)
TEL['Cumulative Return'] = (1 + TEL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TELmean = TEL['returns'].mean()
TELvar = TEL['returns'].var()
TELkurt = TEL['returns'].kurt()
TELskew = TEL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TELbef = web.DataReader('TEL', 'yahoo', start, crashstart)
TELbef['avgreturnbefore'] = TELbef['Close'].pct_change(1)
TELbef['Cumulative Return Before Crash'] = (1 + TELbef['avgreturnbefore']).cumprod()

#period of crash
TELdur = web.DataReader('TEL', 'yahoo', crashstart, crashend)
TELdur['avgreturnduring'] = TELdur['Close'].pct_change(1)
TELdur['Cumulative Return During Crash'] = (1 + TELdur['avgreturnduring']).cumprod()

#period after crash
TELaft = web.DataReader('TEL', 'yahoo', crashend, end)
TELaft['avgreturnafter'] = TELaft['Close'].pct_change(1)
TELaft['Cumulative Return After Crash'] = (1 + TELaft['avgreturnafter']).cumprod()
In [567]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TXN = web.DataReader('TXN', 'yahoo', start, end)
TXN['returns'] = TXN['Close'].pct_change(1)
TXN['Cumulative Return'] = (1 + TXN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TXNmean = TXN['returns'].mean()
TXNvar = TXN['returns'].var()
TXNkurt = TXN['returns'].kurt()
TXNskew = TXN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TXNbef = web.DataReader('TXN', 'yahoo', start, crashstart)
TXNbef['avgreturnbefore'] = TXNbef['Close'].pct_change(1)
TXNbef['Cumulative Return Before Crash'] = (1 + TXNbef['avgreturnbefore']).cumprod()

#period of crash
TXNdur = web.DataReader('TXN', 'yahoo', crashstart, crashend)
TXNdur['avgreturnduring'] = TXNdur['Close'].pct_change(1)
TXNdur['Cumulative Return During Crash'] = (1 + TXNdur['avgreturnduring']).cumprod()

#period after crash
TXNaft = web.DataReader('TXN', 'yahoo', crashend, end)
TXNaft['avgreturnafter'] = TXNaft['Close'].pct_change(1)
TXNaft['Cumulative Return After Crash'] = (1 + TXNaft['avgreturnafter']).cumprod()
In [568]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
VRSN = web.DataReader('VRSN', 'yahoo', start, end)
VRSN['returns'] = VRSN['Close'].pct_change(1)
VRSN['Cumulative Return'] = (1 + VRSN['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
VRSNmean = VRSN['returns'].mean()
VRSNvar = VRSN['returns'].var()
VRSNkurt = VRSN['returns'].kurt()
VRSNskew = VRSN['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
VRSNbef = web.DataReader('VRSN', 'yahoo', start, crashstart)
VRSNbef['avgreturnbefore'] = VRSNbef['Close'].pct_change(1)
VRSNbef['Cumulative Return Before Crash'] = (1 + VRSNbef['avgreturnbefore']).cumprod()

#period of crash
VRSNdur = web.DataReader('VRSN', 'yahoo', crashstart, crashend)
VRSNdur['avgreturnduring'] = VRSNdur['Close'].pct_change(1)
VRSNdur['Cumulative Return During Crash'] = (1 + VRSNdur['avgreturnduring']).cumprod()

#period after crash
VRSNaft = web.DataReader('VRSN', 'yahoo', crashend, end)
VRSNaft['avgreturnafter'] = VRSNaft['Close'].pct_change(1)
VRSNaft['Cumulative Return After Crash'] = (1 + VRSNaft['avgreturnafter']).cumprod()
In [569]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
V = web.DataReader('V', 'yahoo', start, end)
V['returns'] = V['Close'].pct_change(1)
V['Cumulative Return'] = (1 + V['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
Vmean = V['returns'].mean()
Vvar = V['returns'].var()
Vkurt = V['returns'].kurt()
Vskew = V['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
Vbef = web.DataReader('V', 'yahoo', start, crashstart)
Vbef['avgreturnbefore'] = Vbef['Close'].pct_change(1)
Vbef['Cumulative Return Before Crash'] = (1 + Vbef['avgreturnbefore']).cumprod()

#period of crash
Vdur = web.DataReader('V', 'yahoo', crashstart, crashend)
Vdur['avgreturnduring'] = Vdur['Close'].pct_change(1)
Vdur['Cumulative Return During Crash'] = (1 + Vdur['avgreturnduring']).cumprod()

#period after crash
Vaft = web.DataReader('V', 'yahoo', crashend, end)
Vaft['avgreturnafter'] = Vaft['Close'].pct_change(1)
Vaft['Cumulative Return After Crash'] = (1 + Vaft['avgreturnafter']).cumprod()
In [570]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WDC = web.DataReader('WDC', 'yahoo', start, end)
WDC['returns'] = WDC['Close'].pct_change(1)
WDC['Cumulative Return'] = (1 + WDC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WDCmean = WDC['returns'].mean()
WDCvar = WDC['returns'].var()
WDCkurt = WDC['returns'].kurt()
WDCskew = WDC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WDCbef = web.DataReader('WDC', 'yahoo', start, crashstart)
WDCbef['avgreturnbefore'] = WDCbef['Close'].pct_change(1)
WDCbef['Cumulative Return Before Crash'] = (1 + WDCbef['avgreturnbefore']).cumprod()

#period of crash
WDCdur = web.DataReader('WDC', 'yahoo', crashstart, crashend)
WDCdur['avgreturnduring'] = WDCdur['Close'].pct_change(1)
WDCdur['Cumulative Return During Crash'] = (1 + WDCdur['avgreturnduring']).cumprod()

#period after crash
WDCaft = web.DataReader('WDC', 'yahoo', crashend, end)
WDCaft['avgreturnafter'] = WDCaft['Close'].pct_change(1)
WDCaft['Cumulative Return After Crash'] = (1 + WDCaft['avgreturnafter']).cumprod()
In [571]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WU = web.DataReader('WU', 'yahoo', start, end)
WU['returns'] = WU['Close'].pct_change(1)
WU['Cumulative Return'] = (1 + WU['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WUmean = WU['returns'].mean()
WUvar = WU['returns'].var()
WUkurt = WU['returns'].kurt()
WUskew = WU['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WUbef = web.DataReader('WU', 'yahoo', start, crashstart)
WUbef['avgreturnbefore'] = WUbef['Close'].pct_change(1)
WUbef['Cumulative Return Before Crash'] = (1 + WUbef['avgreturnbefore']).cumprod()

#period of crash
WUdur = web.DataReader('WU', 'yahoo', crashstart, crashend)
WUdur['avgreturnduring'] = WUdur['Close'].pct_change(1)
WUdur['Cumulative Return During Crash'] = (1 + WUdur['avgreturnduring']).cumprod()

#period after crash
WUaft = web.DataReader('WU', 'yahoo', crashend, end)
WUaft['avgreturnafter'] = WUaft['Close'].pct_change(1)
WUaft['Cumulative Return After Crash'] = (1 + WUaft['avgreturnafter']).cumprod()
In [572]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
XRX = web.DataReader('XRX', 'yahoo', start, end)
XRX['returns'] = XRX['Close'].pct_change(1)
XRX['Cumulative Return'] = (1 + XRX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
XRXmean = XRX['returns'].mean()
XRXvar = XRX['returns'].var()
XRXkurt = XRX['returns'].kurt()
XRXskew = XRX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
XRXbef = web.DataReader('XRX', 'yahoo', start, crashstart)
XRXbef['avgreturnbefore'] = XRXbef['Close'].pct_change(1)
XRXbef['Cumulative Return Before Crash'] = (1 + XRXbef['avgreturnbefore']).cumprod()

#period of crash
XRXdur = web.DataReader('XRX', 'yahoo', crashstart, crashend)
XRXdur['avgreturnduring'] = XRXdur['Close'].pct_change(1)
XRXdur['Cumulative Return During Crash'] = (1 + XRXdur['avgreturnduring']).cumprod()

#period after crash
XRXaft = web.DataReader('XRX', 'yahoo', crashend, end)
XRXaft['avgreturnafter'] = XRXaft['Close'].pct_change(1)
XRXaft['Cumulative Return After Crash'] = (1 + XRXaft['avgreturnafter']).cumprod()
In [573]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
XLNX = web.DataReader('XLNX', 'yahoo', start, end)
XLNX['returns'] = XLNX['Close'].pct_change(1)
XLNX['Cumulative Return'] = (1 + XLNX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
XLNXmean = XLNX['returns'].mean()
XLNXvar = XLNX['returns'].var()
XLNXkurt = XLNX['returns'].kurt()
XLNXskew = XLNX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
XLNXbef = web.DataReader('XLNX', 'yahoo', start, crashstart)
XLNXbef['avgreturnbefore'] = XLNXbef['Close'].pct_change(1)
XLNXbef['Cumulative Return Before Crash'] = (1 + XLNXbef['avgreturnbefore']).cumprod()

#period of crash
XLNXdur = web.DataReader('XLNX', 'yahoo', crashstart, crashend)
XLNXdur['avgreturnduring'] = XLNXdur['Close'].pct_change(1)
XLNXdur['Cumulative Return During Crash'] = (1 + XLNXdur['avgreturnduring']).cumprod()

#period after crash
XLNXaft = web.DataReader('XLNX', 'yahoo', crashend, end)
XLNXaft['avgreturnafter'] = XLNXaft['Close'].pct_change(1)
XLNXaft['Cumulative Return After Crash'] = (1 + XLNXaft['avgreturnafter']).cumprod()
In [574]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ZBRA = web.DataReader('ZBRA', 'yahoo', start, end)
ZBRA['returns'] = ZBRA['Close'].pct_change(1)
ZBRA['Cumulative Return'] = (1 + ZBRA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ZBRAmean = ZBRA['returns'].mean()
ZBRAvar = ZBRA['returns'].var()
ZBRAkurt = ZBRA['returns'].kurt()
ZBRAskew = ZBRA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ZBRAbef = web.DataReader('ZBRA', 'yahoo', start, crashstart)
ZBRAbef['avgreturnbefore'] = ZBRAbef['Close'].pct_change(1)
ZBRAbef['Cumulative Return Before Crash'] = (1 + ZBRAbef['avgreturnbefore']).cumprod()

#period of crash
ZBRAdur = web.DataReader('ZBRA', 'yahoo', crashstart, crashend)
ZBRAdur['avgreturnduring'] = ZBRAdur['Close'].pct_change(1)
ZBRAdur['Cumulative Return During Crash'] = (1 + ZBRAdur['avgreturnduring']).cumprod()

#period after crash
ZBRAaft = web.DataReader('ZBRA', 'yahoo', crashend, end)
ZBRAaft['avgreturnafter'] = ZBRAaft['Close'].pct_change(1)
ZBRAaft['Cumulative Return After Crash'] = (1 + ZBRAaft['avgreturnafter']).cumprod()
In [575]:
#KDE Plot for Information Technology Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
ACN['returns'].plot(kind = 'kde', label ='ACN', figsize = (10,8), alpha=0.5, title = 'KDE of Information Technology Stocks')
ADBE['returns'].plot(kind = 'kde', label ='ADBE', alpha=0.5)
AMD['returns'].plot(kind = 'kde', label ='AMD', alpha=0.5)
AKAM['returns'].plot(kind = 'kde', label ='AKAM', alpha=0.5)
ADS['returns'].plot(kind = 'kde', label ='ADS', alpha=0.5)
APH['returns'].plot(kind = 'kde', label ='APH', alpha=0.5)
ADI['returns'].plot(kind = 'kde', label ='ADI', alpha=0.5)
ANSS['returns'].plot(kind = 'kde', label ='ANSS', alpha=0.5)
AAPL['returns'].plot(kind = 'kde', label ='AAPL', alpha=0.5)
AMAT['returns'].plot(kind = 'kde', label ='AMAT', alpha=0.5)
ANET['returns'].plot(kind = 'kde', label ='ANET', alpha=0.5)
ADSK['returns'].plot(kind = 'kde', label ='ADSK', alpha=0.5)
ADP['returns'].plot(kind = 'kde', label ='ADP', alpha=0.5)
AVGO['returns'].plot(kind = 'kde', label ='AVGO', alpha=0.5)
BR['returns'].plot(kind = 'kde', label = 'BR', alpha=0.5)
CDNS['returns'].plot(kind = 'kde', label ='CDNS', alpha=0.5)
CDW['returns'].plot(kind = 'kde', label ='CDW', alpha=0.5)
CSCO['returns'].plot(kind = 'kde', label ='CSCO', alpha=0.5)
CTXS['returns'].plot(kind = 'kde', label ='CTXS', alpha=0.5)
CTSH['returns'].plot(kind = 'kde', label ='CTSH', alpha=0.5)
GLW['returns'].plot(kind = 'kde', label ='GLW', alpha=0.5)
DXC['returns'].plot(kind = 'kde', label ='DXC', alpha=0.5)
FFIV['returns'].plot(kind = 'kde', label ='FFIV', alpha=0.5)
FIS['returns'].plot(kind = 'kde', label ='FIS', alpha=0.5)
FISV['returns'].plot(kind = 'kde', label ='FISV', alpha=0.5)
FLT['returns'].plot(kind = 'kde', label ='FLT', alpha=0.5)
FLIR['returns'].plot(kind = 'kde', label ='FLIR', alpha=0.5)
FTNT['returns'].plot(kind = 'kde', label ='FTNT', alpha=0.5)
IT['returns'].plot(kind = 'kde', label ='IT', alpha=0.5)
GPN['returns'].plot(kind = 'kde', label ='GPN', alpha=0.5)
HPE['returns'].plot(kind = 'kde', label ='HPE', alpha=0.5)
HPQ['returns'].plot(kind = 'kde', label ='HPQ', alpha=0.5)
INTC['returns'].plot(kind = 'kde', label ='INTC', alpha=0.5)
IBM['returns'].plot(kind = 'kde', label ='IBM', alpha=0.5)
INTU['returns'].plot(kind = 'kde', label ='INTU', alpha=0.5)
IPGP['returns'].plot(kind = 'kde', label ='IPGP', alpha=0.5)
JKHY['returns'].plot(kind = 'kde', label ='JKHY', alpha=0.5)
JNPR['returns'].plot(kind = 'kde', label ='JNPR', alpha=0.5)
KEYS['returns'].plot(kind = 'kde', label ='KEYS', alpha=0.5)
KLAC['returns'].plot(kind = 'kde', label ='KLAC', alpha=0.5)
LRCX['returns'].plot(kind = 'kde', label ='LRCX', alpha=0.5)
LDOS['returns'].plot(kind = 'kde', label ='LDOS', alpha=0.5)
MA['returns'].plot(kind = 'kde', label ='MA', alpha=0.5)
MXIM['returns'].plot(kind = 'kde', label ='MXIM', alpha=0.5)
MCHP['returns'].plot(kind = 'kde', label ='MCHP', alpha=0.5)
MU['returns'].plot(kind = 'kde', label ='MU', alpha=0.5)
MSFT['returns'].plot(kind = 'kde', label ='MSFT', alpha=0.5)
MSI['returns'].plot(kind = 'kde', label ='MSI', alpha=0.5)
NTAP['returns'].plot(kind = 'kde', label ='NTAP', alpha=0.5)
NLOK['returns'].plot(kind = 'kde', label ='NLOK', alpha=0.5)
NVDA['returns'].plot(kind = 'kde', label ='NVDA', alpha=0.5)
ORCL['returns'].plot(kind = 'kde', label ='ORCL', alpha=0.5)
PAYX['returns'].plot(kind = 'kde', label ='PAYX', alpha=0.5)
PAYC['returns'].plot(kind = 'kde', label ='PAYC', alpha=0.5)
PYPL['returns'].plot(kind = 'kde', label ='PYPL', alpha=0.5)
QRVO['returns'].plot(kind = 'kde', label ='QRVO', alpha=0.5)
QCOM['returns'].plot(kind = 'kde', label ='QCOM', alpha=0.5)
CRM['returns'].plot(kind = 'kde', label ='CRM', alpha=0.5)
STX['returns'].plot(kind = 'kde', label ='STX', alpha=0.5)
NOW['returns'].plot(kind = 'kde', label ='NOW', alpha=0.5)
SWKS['returns'].plot(kind = 'kde', label ='SWKS', alpha=0.5)
SNPS['returns'].plot(kind = 'kde', label ='SNPS', alpha=0.5)
TEL['returns'].plot(kind = 'kde', label ='TEL', alpha=0.5)
TXN['returns'].plot(kind = 'kde', label ='TXN', alpha=0.5)
VRSN['returns'].plot(kind = 'kde', label ='VRSN', alpha=0.5)
V['returns'].plot(kind = 'kde', label ='V', alpha=0.5)
WDC['returns'].plot(kind = 'kde', label ='WDC', alpha=0.5)
WU['returns'].plot(kind = 'kde', label ='WU', alpha=0.5)
XRX['returns'].plot(kind = 'kde', label ='XRX', alpha=0.5)
XLNX['returns'].plot(kind = 'kde', label ='XLNX', alpha=0.5)
ZBRA['returns'].plot(kind = 'kde', label ='ZBRA', alpha=0.5)

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [576]:
#Boxplots comparing returns for Information Technology Sector
box_df = pd.concat([sp['returns'],ACN['returns'],ADBE['returns'],AMD['returns'],AKAM['returns'],ADS['returns'],APH['returns'],ADI['returns'],ANSS['returns'],AAPL['returns'],AMAT['returns'],ANET['returns'],ADSK['returns'],ADP['returns'],AVGO['returns'],BR['returns'],CDNS['returns'],CDW['returns'],CSCO['returns'],CTXS['returns'],CTSH['returns'],GLW['returns'],DXC['returns'],FFIV['returns'],FIS['returns'],FISV['returns'],FLT['returns'],FLIR['returns'],FTNT['returns'],IT['returns'],GPN['returns'],HPE['returns'],HPQ['returns'],INTC['returns'],IBM['returns'],INTU['returns'],IPGP['returns'],JKHY['returns'],JNPR['returns'],KEYS['returns'],KLAC['returns'],LRCX['returns'],LDOS['returns'],MA['returns'],MXIM['returns'],MCHP['returns'],MU['returns'],MSFT['returns'],MSI['returns'],NTAP['returns'],NLOK['returns'],NVDA['returns'],ORCL['returns'],PAYX['returns'],PAYC['returns'],PYPL['returns'],QRVO['returns'],QCOM['returns'],CRM['returns'],STX['returns'],NOW['returns'],SWKS['returns'],SNPS['returns'],TEL['returns'],TXN['returns'],VRSN['returns'],V['returns'],WDC['returns'],WU['returns'],XRX['returns'],XLNX['returns'],ZBRA['returns']], axis=1)
box_df.columns = ['S&P 500','ACN','ADBE','AMD','AKAM','ADS','APH','ADI','ANSS','AAPL','AMAT','ANET','ADSK','ADP','AVGO','BR','CDNS','CDW','CSCO','CTXS','CTSH','GLW','DXC','FFIV','FIS','FISV','FLT','FLIR','FTNT','IT','GPN','HPE','HPQ','INTC','IBM','INTU','IPGP','JKHY','JNPR','KEYS','KLAC','LRCX','LDOS','MA','MXIM','MCHP','MU','MSFT','MSI','NTAP','NLOK','NVDA','ORCL','PAYX','PAYC','PYPL','QRVO','QCOM','CRM','STX','NOW','SWKS','SNPS','TEL','TXN','VRSN','V','WDC','WU','XRX','XLNX','ZBRA',]
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Information Technology Sector Returns')
Out[576]:
<matplotlib.axes._subplots.AxesSubplot at 0x13fa121c2b0>
In [577]:
#Cumulative Returns of Information Technology Stocks for Whole period

sp['Cumulative Return'].plot(label="S&P 500")
ACN['Cumulative Return'].plot(label='ACN', figsize=(16,8), title = 'Cumulative Return of Information Technology Stocks')
ADBE['Cumulative Return'].plot(label="ADBE")
AMD['Cumulative Return'].plot(label="AMD")
AKAM['Cumulative Return'].plot(label="AKAM")
ADS['Cumulative Return'].plot(label="ADS")
APH['Cumulative Return'].plot(label="APH")
ADI['Cumulative Return'].plot(label="ADI")
ANSS['Cumulative Return'].plot(label="ANSS")
AAPL['Cumulative Return'].plot(label="AAPL")
AMAT['Cumulative Return'].plot(label="AMAT")
ANET['Cumulative Return'].plot(label="ANET")
ADSK['Cumulative Return'].plot(label="ADSK")
ADP['Cumulative Return'].plot(label="ADP")
AVGO['Cumulative Return'].plot(label="AVGO")
BR['Cumulative Return'].plot(label="BR")
CDNS['Cumulative Return'].plot(label="CDNS")
CDW['Cumulative Return'].plot(label="CDW")
CSCO['Cumulative Return'].plot(label="CSCO")
CTXS['Cumulative Return'].plot(label="CTXS")
CTSH['Cumulative Return'].plot(label="CTSH")
GLW['Cumulative Return'].plot(label="GLW")
DXC['Cumulative Return'].plot(label="DXC")
FFIV['Cumulative Return'].plot(label="FFIV")
FIS['Cumulative Return'].plot(label="FIS")
FISV['Cumulative Return'].plot(label="FISV")
FLT['Cumulative Return'].plot(label="FLT")
FLIR['Cumulative Return'].plot(label="FLIR")
FTNT['Cumulative Return'].plot(label="FTNT")
IT['Cumulative Return'].plot(label="IT")
GPN['Cumulative Return'].plot(label="GPN")
HPE['Cumulative Return'].plot(label="HPE")
HPQ['Cumulative Return'].plot(label="HPQ")
INTC['Cumulative Return'].plot(label="INTC")
IBM['Cumulative Return'].plot(label="IBM")
INTU['Cumulative Return'].plot(label="INTU")
IPGP['Cumulative Return'].plot(label="IPGP")
JKHY['Cumulative Return'].plot(label="JKHY")
JNPR['Cumulative Return'].plot(label="JNPR")
KEYS['Cumulative Return'].plot(label="KEYS")
KLAC['Cumulative Return'].plot(label="KLAC")
LRCX['Cumulative Return'].plot(label="LRCX")
LDOS['Cumulative Return'].plot(label="LDOS")
MA['Cumulative Return'].plot(label="MA")
MXIM['Cumulative Return'].plot(label="MXIM")
MCHP['Cumulative Return'].plot(label="MCHP")
MU['Cumulative Return'].plot(label="MU")
MSFT['Cumulative Return'].plot(label="MSFT")
MSI['Cumulative Return'].plot(label="MSI")
NTAP['Cumulative Return'].plot(label="NTAP")
NLOK['Cumulative Return'].plot(label="NLOK")
NVDA['Cumulative Return'].plot(label="NVDA")
ORCL['Cumulative Return'].plot(label="ORCL")
PAYX['Cumulative Return'].plot(label="PAYX")
PAYC['Cumulative Return'].plot(label="PAYC")
PYPL['Cumulative Return'].plot(label="PYPL")
QRVO['Cumulative Return'].plot(label="QRVO")
QCOM['Cumulative Return'].plot(label="QCOM")
CRM['Cumulative Return'].plot(label="CRM")
STX['Cumulative Return'].plot(label="STX")
NOW['Cumulative Return'].plot(label="NOW")
SWKS['Cumulative Return'].plot(label="SWKS")
SNPS['Cumulative Return'].plot(label="SNPS")
TEL['Cumulative Return'].plot(label="TEL")
TXN['Cumulative Return'].plot(label="TXN")
VRSN['Cumulative Return'].plot(label="VRSN")
V['Cumulative Return'].plot(label="V")
WDC['Cumulative Return'].plot(label="WDC")
WU['Cumulative Return'].plot(label="WU")
XRX['Cumulative Return'].plot(label="XRX")
XLNX['Cumulative Return'].plot(label="XLNX")
ZBRA['Cumulative Return'].plot(label="ZBRA")


plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [578]:
#Sorting by Cumulative Return


cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],ACN['Cumulative Return'].iloc[-1],ADBE['Cumulative Return'].iloc[-1],AMD['Cumulative Return'].iloc[-1],AKAM['Cumulative Return'].iloc[-1],ADS['Cumulative Return'].iloc[-1],APH['Cumulative Return'].iloc[-1],ADI['Cumulative Return'].iloc[-1],ANSS['Cumulative Return'].iloc[-1],AAPL['Cumulative Return'].iloc[-1],AMAT['Cumulative Return'].iloc[-1],ANET['Cumulative Return'].iloc[-1],ADSK['Cumulative Return'].iloc[-1],ADP['Cumulative Return'].iloc[-1],AVGO['Cumulative Return'].iloc[-1],BR['Cumulative Return'].iloc[-1],CDNS['Cumulative Return'].iloc[-1],CDW['Cumulative Return'].iloc[-1],CSCO['Cumulative Return'].iloc[-1],CTXS['Cumulative Return'].iloc[-1],CTSH['Cumulative Return'].iloc[-1],GLW['Cumulative Return'].iloc[-1],DXC['Cumulative Return'].iloc[-1],FFIV['Cumulative Return'].iloc[-1],FIS['Cumulative Return'].iloc[-1],FISV['Cumulative Return'].iloc[-1],FLT['Cumulative Return'].iloc[-1],FLIR['Cumulative Return'].iloc[-1],FTNT['Cumulative Return'].iloc[-1],IT['Cumulative Return'].iloc[-1],GPN['Cumulative Return'].iloc[-1],HPE['Cumulative Return'].iloc[-1],HPQ['Cumulative Return'].iloc[-1],INTC['Cumulative Return'].iloc[-1],IBM['Cumulative Return'].iloc[-1],INTU['Cumulative Return'].iloc[-1],IPGP['Cumulative Return'].iloc[-1],JKHY['Cumulative Return'].iloc[-1],JNPR['Cumulative Return'].iloc[-1],KEYS['Cumulative Return'].iloc[-1],KLAC['Cumulative Return'].iloc[-1],LRCX['Cumulative Return'].iloc[-1],LDOS['Cumulative Return'].iloc[-1],MA['Cumulative Return'].iloc[-1],MXIM['Cumulative Return'].iloc[-1],MCHP['Cumulative Return'].iloc[-1],MU['Cumulative Return'].iloc[-1],MSFT['Cumulative Return'].iloc[-1],MSI['Cumulative Return'].iloc[-1],NTAP['Cumulative Return'].iloc[-1],NLOK['Cumulative Return'].iloc[-1],NVDA['Cumulative Return'].iloc[-1],ORCL['Cumulative Return'].iloc[-1],PAYX['Cumulative Return'].iloc[-1],PAYC['Cumulative Return'].iloc[-1],PYPL['Cumulative Return'].iloc[-1],QRVO['Cumulative Return'].iloc[-1],QCOM['Cumulative Return'].iloc[-1],CRM['Cumulative Return'].iloc[-1],STX['Cumulative Return'].iloc[-1],NOW['Cumulative Return'].iloc[-1],SWKS['Cumulative Return'].iloc[-1],SNPS['Cumulative Return'].iloc[-1],TEL['Cumulative Return'].iloc[-1],TXN['Cumulative Return'].iloc[-1],VRSN['Cumulative Return'].iloc[-1],V['Cumulative Return'].iloc[-1],WDC['Cumulative Return'].iloc[-1],WU['Cumulative Return'].iloc[-1],XRX['Cumulative Return'].iloc[-1],XLNX['Cumulative Return'].iloc[-1],ZBRA['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],ACNbef['Cumulative Return Before Crash'].iloc[-1],ADBEbef['Cumulative Return Before Crash'].iloc[-1],AMDbef['Cumulative Return Before Crash'].iloc[-1],AKAMbef['Cumulative Return Before Crash'].iloc[-1],ADSbef['Cumulative Return Before Crash'].iloc[-1],APHbef['Cumulative Return Before Crash'].iloc[-1],ADIbef['Cumulative Return Before Crash'].iloc[-1],ANSSbef['Cumulative Return Before Crash'].iloc[-1],AAPLbef['Cumulative Return Before Crash'].iloc[-1],AMATbef['Cumulative Return Before Crash'].iloc[-1],ANETbef['Cumulative Return Before Crash'].iloc[-1],ADSKbef['Cumulative Return Before Crash'].iloc[-1],ADPbef['Cumulative Return Before Crash'].iloc[-1],AVGObef['Cumulative Return Before Crash'].iloc[-1],BRbef['Cumulative Return Before Crash'].iloc[-1],CDNSbef['Cumulative Return Before Crash'].iloc[-1],CDWbef['Cumulative Return Before Crash'].iloc[-1],CSCObef['Cumulative Return Before Crash'].iloc[-1],CTXSbef['Cumulative Return Before Crash'].iloc[-1],CTSHbef['Cumulative Return Before Crash'].iloc[-1],GLWbef['Cumulative Return Before Crash'].iloc[-1],DXCbef['Cumulative Return Before Crash'].iloc[-1],FFIVbef['Cumulative Return Before Crash'].iloc[-1],FISbef['Cumulative Return Before Crash'].iloc[-1],FISVbef['Cumulative Return Before Crash'].iloc[-1],FLTbef['Cumulative Return Before Crash'].iloc[-1],FLIRbef['Cumulative Return Before Crash'].iloc[-1],FTNTbef['Cumulative Return Before Crash'].iloc[-1],ITbef['Cumulative Return Before Crash'].iloc[-1],GPNbef['Cumulative Return Before Crash'].iloc[-1],HPEbef['Cumulative Return Before Crash'].iloc[-1],HPQbef['Cumulative Return Before Crash'].iloc[-1],INTCbef['Cumulative Return Before Crash'].iloc[-1],IBMbef['Cumulative Return Before Crash'].iloc[-1],INTUbef['Cumulative Return Before Crash'].iloc[-1],IPGPbef['Cumulative Return Before Crash'].iloc[-1],JKHYbef['Cumulative Return Before Crash'].iloc[-1],JNPRbef['Cumulative Return Before Crash'].iloc[-1],KEYSbef['Cumulative Return Before Crash'].iloc[-1],KLACbef['Cumulative Return Before Crash'].iloc[-1],LRCXbef['Cumulative Return Before Crash'].iloc[-1],LDOSbef['Cumulative Return Before Crash'].iloc[-1],MAbef['Cumulative Return Before Crash'].iloc[-1],MXIMbef['Cumulative Return Before Crash'].iloc[-1],MCHPbef['Cumulative Return Before Crash'].iloc[-1],MUbef['Cumulative Return Before Crash'].iloc[-1],MSFTbef['Cumulative Return Before Crash'].iloc[-1],MSIbef['Cumulative Return Before Crash'].iloc[-1],NTAPbef['Cumulative Return Before Crash'].iloc[-1],NLOKbef['Cumulative Return Before Crash'].iloc[-1],NVDAbef['Cumulative Return Before Crash'].iloc[-1],ORCLbef['Cumulative Return Before Crash'].iloc[-1],PAYXbef['Cumulative Return Before Crash'].iloc[-1],PAYCbef['Cumulative Return Before Crash'].iloc[-1],PYPLbef['Cumulative Return Before Crash'].iloc[-1],QRVObef['Cumulative Return Before Crash'].iloc[-1],QCOMbef['Cumulative Return Before Crash'].iloc[-1],CRMbef['Cumulative Return Before Crash'].iloc[-1],STXbef['Cumulative Return Before Crash'].iloc[-1],NOWbef['Cumulative Return Before Crash'].iloc[-1],SWKSbef['Cumulative Return Before Crash'].iloc[-1],SNPSbef['Cumulative Return Before Crash'].iloc[-1],TELbef['Cumulative Return Before Crash'].iloc[-1],TXNbef['Cumulative Return Before Crash'].iloc[-1],VRSNbef['Cumulative Return Before Crash'].iloc[-1],Vbef['Cumulative Return Before Crash'].iloc[-1],WDCbef['Cumulative Return Before Crash'].iloc[-1],WUbef['Cumulative Return Before Crash'].iloc[-1],XRXbef['Cumulative Return Before Crash'].iloc[-1],XLNXbef['Cumulative Return Before Crash'].iloc[-1],ZBRAbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],ACNdur['Cumulative Return During Crash'].iloc[-1],ADBEdur['Cumulative Return During Crash'].iloc[-1],AMDdur['Cumulative Return During Crash'].iloc[-1],AKAMdur['Cumulative Return During Crash'].iloc[-1],ADSdur['Cumulative Return During Crash'].iloc[-1],APHdur['Cumulative Return During Crash'].iloc[-1],ADIdur['Cumulative Return During Crash'].iloc[-1],ANSSdur['Cumulative Return During Crash'].iloc[-1],AAPLdur['Cumulative Return During Crash'].iloc[-1],AMATdur['Cumulative Return During Crash'].iloc[-1],ANETdur['Cumulative Return During Crash'].iloc[-1],ADSKdur['Cumulative Return During Crash'].iloc[-1],ADPdur['Cumulative Return During Crash'].iloc[-1],AVGOdur['Cumulative Return During Crash'].iloc[-1],BRdur['Cumulative Return During Crash'].iloc[-1],CDNSdur['Cumulative Return During Crash'].iloc[-1],CDWdur['Cumulative Return During Crash'].iloc[-1],CSCOdur['Cumulative Return During Crash'].iloc[-1],CTXSdur['Cumulative Return During Crash'].iloc[-1],CTSHdur['Cumulative Return During Crash'].iloc[-1],GLWdur['Cumulative Return During Crash'].iloc[-1],DXCdur['Cumulative Return During Crash'].iloc[-1],FFIVdur['Cumulative Return During Crash'].iloc[-1],FISdur['Cumulative Return During Crash'].iloc[-1],FISVdur['Cumulative Return During Crash'].iloc[-1],FLTdur['Cumulative Return During Crash'].iloc[-1],FLIRdur['Cumulative Return During Crash'].iloc[-1],FTNTdur['Cumulative Return During Crash'].iloc[-1],ITdur['Cumulative Return During Crash'].iloc[-1],GPNdur['Cumulative Return During Crash'].iloc[-1],HPEdur['Cumulative Return During Crash'].iloc[-1],HPQdur['Cumulative Return During Crash'].iloc[-1],INTCdur['Cumulative Return During Crash'].iloc[-1],IBMdur['Cumulative Return During Crash'].iloc[-1],INTUdur['Cumulative Return During Crash'].iloc[-1],IPGPdur['Cumulative Return During Crash'].iloc[-1],JKHYdur['Cumulative Return During Crash'].iloc[-1],JNPRdur['Cumulative Return During Crash'].iloc[-1],KEYSdur['Cumulative Return During Crash'].iloc[-1],KLACdur['Cumulative Return During Crash'].iloc[-1],LRCXdur['Cumulative Return During Crash'].iloc[-1],LDOSdur['Cumulative Return During Crash'].iloc[-1],MAdur['Cumulative Return During Crash'].iloc[-1],MXIMdur['Cumulative Return During Crash'].iloc[-1],MCHPdur['Cumulative Return During Crash'].iloc[-1],MUdur['Cumulative Return During Crash'].iloc[-1],MSFTdur['Cumulative Return During Crash'].iloc[-1],MSIdur['Cumulative Return During Crash'].iloc[-1],NTAPdur['Cumulative Return During Crash'].iloc[-1],NLOKdur['Cumulative Return During Crash'].iloc[-1],NVDAdur['Cumulative Return During Crash'].iloc[-1],ORCLdur['Cumulative Return During Crash'].iloc[-1],PAYXdur['Cumulative Return During Crash'].iloc[-1],PAYCdur['Cumulative Return During Crash'].iloc[-1],PYPLdur['Cumulative Return During Crash'].iloc[-1],QRVOdur['Cumulative Return During Crash'].iloc[-1],QCOMdur['Cumulative Return During Crash'].iloc[-1],CRMdur['Cumulative Return During Crash'].iloc[-1],STXdur['Cumulative Return During Crash'].iloc[-1],NOWdur['Cumulative Return During Crash'].iloc[-1],SWKSdur['Cumulative Return During Crash'].iloc[-1],SNPSdur['Cumulative Return During Crash'].iloc[-1],TELdur['Cumulative Return During Crash'].iloc[-1],TXNdur['Cumulative Return During Crash'].iloc[-1],VRSNdur['Cumulative Return During Crash'].iloc[-1],Vdur['Cumulative Return During Crash'].iloc[-1],WDCdur['Cumulative Return During Crash'].iloc[-1],WUdur['Cumulative Return During Crash'].iloc[-1],XRXdur['Cumulative Return During Crash'].iloc[-1],XLNXdur['Cumulative Return During Crash'].iloc[-1],ZBRAdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],ACNaft['Cumulative Return After Crash'].iloc[-1],ADBEaft['Cumulative Return After Crash'].iloc[-1],AMDaft['Cumulative Return After Crash'].iloc[-1],AKAMaft['Cumulative Return After Crash'].iloc[-1],ADSaft['Cumulative Return After Crash'].iloc[-1],APHaft['Cumulative Return After Crash'].iloc[-1],ADIaft['Cumulative Return After Crash'].iloc[-1],ANSSaft['Cumulative Return After Crash'].iloc[-1],AAPLaft['Cumulative Return After Crash'].iloc[-1],AMATaft['Cumulative Return After Crash'].iloc[-1],ANETaft['Cumulative Return After Crash'].iloc[-1],ADSKaft['Cumulative Return After Crash'].iloc[-1],ADPaft['Cumulative Return After Crash'].iloc[-1],AVGOaft['Cumulative Return After Crash'].iloc[-1],BRaft['Cumulative Return After Crash'].iloc[-1],CDNSaft['Cumulative Return After Crash'].iloc[-1],CDWaft['Cumulative Return After Crash'].iloc[-1],CSCOaft['Cumulative Return After Crash'].iloc[-1],CTXSaft['Cumulative Return After Crash'].iloc[-1],CTSHaft['Cumulative Return After Crash'].iloc[-1],GLWaft['Cumulative Return After Crash'].iloc[-1],DXCaft['Cumulative Return After Crash'].iloc[-1],FFIVaft['Cumulative Return After Crash'].iloc[-1],FISaft['Cumulative Return After Crash'].iloc[-1],FISVaft['Cumulative Return After Crash'].iloc[-1],FLTaft['Cumulative Return After Crash'].iloc[-1],FLIRaft['Cumulative Return After Crash'].iloc[-1],FTNTaft['Cumulative Return After Crash'].iloc[-1],ITaft['Cumulative Return After Crash'].iloc[-1],GPNaft['Cumulative Return After Crash'].iloc[-1],HPEaft['Cumulative Return After Crash'].iloc[-1],HPQaft['Cumulative Return After Crash'].iloc[-1],INTCaft['Cumulative Return After Crash'].iloc[-1],IBMaft['Cumulative Return After Crash'].iloc[-1],INTUaft['Cumulative Return After Crash'].iloc[-1],IPGPaft['Cumulative Return After Crash'].iloc[-1],JKHYaft['Cumulative Return After Crash'].iloc[-1],JNPRaft['Cumulative Return After Crash'].iloc[-1],KEYSaft['Cumulative Return After Crash'].iloc[-1],KLACaft['Cumulative Return After Crash'].iloc[-1],LRCXaft['Cumulative Return After Crash'].iloc[-1],LDOSaft['Cumulative Return After Crash'].iloc[-1],MAaft['Cumulative Return After Crash'].iloc[-1],MXIMaft['Cumulative Return After Crash'].iloc[-1],MCHPaft['Cumulative Return After Crash'].iloc[-1],MUaft['Cumulative Return After Crash'].iloc[-1],MSFTaft['Cumulative Return After Crash'].iloc[-1],MSIaft['Cumulative Return After Crash'].iloc[-1],NTAPaft['Cumulative Return After Crash'].iloc[-1],NLOKaft['Cumulative Return After Crash'].iloc[-1],NVDAaft['Cumulative Return After Crash'].iloc[-1],ORCLaft['Cumulative Return After Crash'].iloc[-1],PAYXaft['Cumulative Return After Crash'].iloc[-1],PAYCaft['Cumulative Return After Crash'].iloc[-1],PYPLaft['Cumulative Return After Crash'].iloc[-1],QRVOaft['Cumulative Return After Crash'].iloc[-1],QCOMaft['Cumulative Return After Crash'].iloc[-1],CRMaft['Cumulative Return After Crash'].iloc[-1],STXaft['Cumulative Return After Crash'].iloc[-1],NOWaft['Cumulative Return After Crash'].iloc[-1],SWKSaft['Cumulative Return After Crash'].iloc[-1],SNPSaft['Cumulative Return After Crash'].iloc[-1],TELaft['Cumulative Return After Crash'].iloc[-1],TXNaft['Cumulative Return After Crash'].iloc[-1],VRSNaft['Cumulative Return After Crash'].iloc[-1],Vaft['Cumulative Return After Crash'].iloc[-1],WDCaft['Cumulative Return After Crash'].iloc[-1],WUaft['Cumulative Return After Crash'].iloc[-1],XRXaft['Cumulative Return After Crash'].iloc[-1],XLNXaft['Cumulative Return After Crash'].iloc[-1],ZBRAaft['Cumulative Return After Crash'].iloc[-1]]}                         

itdf2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','ACN','ADBE','AMD','AKAM','ADS','APH','ADI','ANSS','AAPL','AMAT','ANET','ADSK','ADP','AVGO','BR','CDNS','CDW','CSCO','CTXS','CTSH','GLW','DXC','FFIV','FIS','FISV','FLT','FLIR','FTNT','IT','GPN','HPE','HPQ','INTC','IBM','INTU','IPGP','JKHY','JNPR','KEYS','KLAC','LRCX','LDOS','MA','MXIM','MCHP','MU','MSFT','MSI','NTAP','NLOK','NVDA','ORCL','PAYX','PAYC','PYPL','QRVO','QCOM','CRM','STX','NOW','SWKS','SNPS','TEL','TXN','VRSN','V','WDC','URI','XRX','XLNX','ZBRA'])

pd.DataFrame(itdf2)
Out[578]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
ACN 1.044644 1.071751 0.671763 1.450971
ADBE 1.298800 1.252719 0.810824 1.278680
AMD 1.337868 1.442933 0.727082 1.275216
AKAM 1.145436 1.174014 0.843008 1.157353
... ... ... ... ...
WDC 1.021501 1.455734 0.497683 1.409951
URI 0.917414 0.990284 0.700000 1.323450
XRX 0.481356 0.959061 0.438010 1.145872
XLNX 1.066874 0.995786 0.801871 1.336111
ZBRA 1.096126 0.958572 0.703362 1.625761

72 rows × 4 columns

In [579]:
itdf2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[579]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
ADS 0.578523 0.988111 0.300990 1.945197
PAYC 1.221815 1.149754 0.576612 1.842964
PYPL 1.485714 1.166000 0.696398 1.829697
SWKS 1.377859 1.189031 0.637872 1.816678
SNPS 1.345269 1.140210 0.699510 1.686670
... ... ... ... ...
CRM 1.111552 1.236080 0.726727 1.237404
ORCL 0.992097 1.020217 0.796073 1.221543
AKAM 1.145436 1.174014 0.843008 1.157353
XRX 0.481356 0.959061 0.438010 1.145872
CTXS 1.255073 1.078470 1.016977 1.144326

72 rows × 4 columns

In [580]:
#Table of Key Statistics of Information Technology Stocks
import pandas as pd
import statistics as stats

itmeans = [ACNmean,ADBEmean,AMDmean,AKAMmean,ADSmean,APHmean,ADImean,ANSSmean,AAPLmean,AMATmean,ANETmean,ADSKmean,ADPmean,AVGOmean,BRmean,CDNSmean,CDWmean,CSCOmean,CTXSmean,CTSHmean,GLWmean,DXCmean,FFIVmean,FISmean,FISVmean,FLTmean,FLIRmean,FTNTmean,ITmean,GPNmean,HPEmean,HPQmean,INTCmean,IBMmean,INTUmean,IPGPmean,JKHYmean,JNPRmean,KEYSmean,KLACmean,LRCXmean,LDOSmean,MAmean,MXIMmean,MCHPmean,MUmean,MSFTmean,MSImean,NTAPmean,NLOKmean,NVDAmean,ORCLmean,PAYXmean,PAYCmean,PYPLmean,QRVOmean,QCOMmean,CRMmean,STXmean,NOWmean,SWKSmean,SNPSmean,TELmean,TXNmean,VRSNmean,Vmean,WDCmean,WUmean,XRXmean,XLNXmean,ZBRAmean]
itaveragemean = stats.mean(itmeans)
itvariances = [ACNvar,ADBEvar,AMDvar,AKAMvar,ADSvar,APHvar,ADIvar,ANSSvar,AAPLvar,AMATvar,ANETvar,ADSKvar,ADPvar,AVGOvar,BRvar,CDNSvar,CDWvar,CSCOvar,CTXSvar,CTSHvar,GLWvar,DXCvar,FFIVvar,FISvar,FISVvar,FLTvar,FLIRvar,FTNTvar,ITvar,GPNvar,HPEvar,HPQvar,INTCvar,IBMvar,INTUvar,IPGPvar,JKHYvar,JNPRvar,KEYSvar,KLACvar,LRCXvar,LDOSvar,MAvar,MXIMvar,MCHPvar,MUvar,MSFTvar,MSIvar,NTAPvar,NLOKvar,NVDAvar,ORCLvar,PAYXvar,PAYCvar,PYPLvar,QRVOvar,QCOMvar,CRMvar,STXvar,NOWvar,SWKSvar,SNPSvar,TELvar,TXNvar,VRSNvar,Vvar,WDCvar,WUvar,XRXvar,XLNXvar,ZBRAvar]
itaveragevar = stats.mean(itvariances)
itkurtoses = [ACNkurt,ADBEkurt,AMDkurt,AKAMkurt,ADSkurt,APHkurt,ADIkurt,ANSSkurt,AAPLkurt,AMATkurt,ANETkurt,ADSKkurt,ADPkurt,AVGOkurt,BRkurt,CDNSkurt,CDWkurt,CSCOkurt,CTXSkurt,CTSHkurt,GLWkurt,DXCkurt,FFIVkurt,FISkurt,FISVkurt,FLTkurt,FLIRkurt,FTNTkurt,ITkurt,GPNkurt,HPEkurt,HPQkurt,INTCkurt,IBMkurt,INTUkurt,IPGPkurt,JKHYkurt,JNPRkurt,KEYSkurt,KLACkurt,LRCXkurt,LDOSkurt,MAkurt,MXIMkurt,MCHPkurt,MUkurt,MSFTkurt,MSIkurt,NTAPkurt,NLOKkurt,NVDAkurt,ORCLkurt,PAYXkurt,PAYCkurt,PYPLkurt,QRVOkurt,QCOMkurt,CRMkurt,STXkurt,NOWkurt,SWKSkurt,SNPSkurt,TELkurt,TXNkurt,VRSNkurt,Vkurt,WDCkurt,WUkurt,XRXkurt,XLNXkurt,ZBRAkurt]
itaveragekurt = stats.mean(itkurtoses)
itskews = [ACNskew,ADBEskew,AMDskew,AKAMskew,ADSskew,APHskew,ADIskew,ANSSskew,AAPLskew,AMATskew,ANETskew,ADSKskew,ADPskew,AVGOskew,BRskew,CDNSskew,CDWskew,CSCOskew,CTXSskew,CTSHskew,GLWskew,DXCskew,FFIVskew,FISskew,FISVskew,FLTskew,FLIRskew,FTNTskew,ITskew,GPNskew,HPEskew,HPQskew,INTCskew,IBMskew,INTUskew,IPGPskew,JKHYskew,JNPRskew,KEYSskew,KLACskew,LRCXskew,LDOSskew,MAskew,MXIMskew,MCHPskew,MUskew,MSFTskew,MSIskew,NTAPskew,NLOKskew,NVDAskew,ORCLskew,PAYXskew,PAYCskew,PYPLskew,QRVOskew,QCOMskew,CRMskew,STXskew,NOWskew,SWKSskew,SNPSskew,TELskew,TXNskew,VRSNskew,Vskew,WDCskew,WUskew,XRXskew,XLNXskew,ZBRAskew]
itaverageskew = stats.mean(itskews)

Information_Technology = {'Mean': [[spmean],[itaveragemean],[ACNmean], [ADBEmean],[AMDmean],[AKAMmean],[ADSmean],[APHmean],[ADImean],[ANSSmean],[AAPLmean],[AMATmean],[ANETmean],[ADSKmean],[ADPmean],[AVGOmean],[BRmean],[CDNSmean],[CDWmean],[CSCOmean],[CTXSmean],[CTSHmean],[GLWmean],[DXCmean],[FFIVmean],[FISmean],[FISVmean],[FLTmean],[FLIRmean],[FTNTmean], [ITmean],[GPNmean],[HPEmean],[HPQmean],[INTCmean],[IBMmean],[INTUmean],[IPGPmean],[JKHYmean],[JNPRmean],[KEYSmean],[KLACmean],[LRCXmean],[LDOSmean],[MAmean],[MXIMmean],[MCHPmean],[MUmean],[MSFTmean],[MSImean],[NTAPmean],[NLOKmean],[NVDAmean],[ORCLmean],[PAYXmean],[PAYCmean],[PYPLmean],[QRVOmean],[QCOMmean],[CRMmean],[STXmean],[NOWmean],[SWKSmean],[SNPSmean],[TELmean],[TXNmean],[VRSNmean],[Vmean],[WDCmean],[WUmean],[XRXmean],[XLNXmean],[ZBRAmean]], 
        'Variance': [[spvar],[itaveragevar],[ACNvar],[ADBEvar],[AMDvar],[AKAMvar],[ADSvar],[APHvar],[ADIvar],[ANSSvar],[AAPLvar],[AMATvar],[ANETvar],[ADSKvar],[ADPvar],[AVGOvar],[BRvar],[CDNSvar],[CDWvar],[CSCOvar],[CTXSvar],[CTSHvar],[GLWvar],[DXCvar],[FFIVvar],[FISvar],[FISVvar],[FLTvar],[FLIRvar],[FTNTvar],[ITvar],[GPNvar],[HPEvar],[HPQvar],[INTCvar],[IBMvar],[INTUvar],[IPGPvar],[JKHYvar],[JNPRvar],[KEYSvar],[KLACvar],[LRCXvar],[LDOSvar],[MAvar],[MXIMvar],[MCHPvar],[MUvar],[MSFTvar],[MSIvar],[NTAPvar],[NLOKvar],[NVDAvar],[ORCLvar],[PAYXvar],[PAYCvar],[PYPLvar],[QRVOvar],[QCOMvar],[CRMvar],[STXvar],[NOWvar],[SWKSvar],[SNPSvar],[TELvar],[TXNvar],[VRSNvar],[Vvar],[WDCvar],[WUvar],[XRXvar],[XLNXvar],[ZBRAvar]], 
          'Kurtosis': [[spkurt],[itaveragekurt],[ACNkurt],[ADBEkurt],[AMDkurt],[AKAMkurt],[ADSkurt],[APHkurt],[ADIkurt],[ANSSkurt],[AAPLkurt],[AMATkurt],[ANETkurt],[ADSKkurt],[ADPkurt],[AVGOkurt],[BRkurt],[CDNSkurt],[CDWkurt],[CSCOkurt],[CTXSkurt],[CTSHkurt],[GLWkurt],[DXCkurt],[FFIVkurt],[FISkurt],[FISVkurt],[FLTkurt],[FLIRkurt],[FTNTkurt],[ITkurt],[GPNkurt],[HPEkurt],[HPQkurt],[INTCkurt],[IBMkurt],[INTUkurt],[IPGPkurt],[JKHYkurt],[JNPRkurt],[KEYSkurt],[KLACkurt],[LRCXkurt],[LDOSkurt],[MAkurt],[MXIMkurt],[MCHPkurt],[MUkurt],[MSFTkurt],[MSIkurt],[NTAPkurt],[NLOKkurt],[NVDAkurt],[ORCLkurt],[PAYXkurt],[PAYCkurt],[PYPLkurt],[QRVOkurt],[QCOMkurt],[CRMkurt],[STXkurt],[NOWkurt],[SWKSkurt],[SNPSkurt],[TELkurt],[TXNkurt],[VRSNkurt],[Vkurt],[WDCkurt],[WUkurt],[XRXkurt],[XLNXkurt],[ZBRAkurt]],
          'Skew': [[spskew],[itaverageskew],[ACNskew],[ADBEskew],[AMDskew],[AKAMskew],[ADSskew],[APHskew],[ADIskew],[ANSSskew],[AAPLskew],[AMATskew],[ANETskew],[ADSKskew],[ADPskew],[AVGOskew],[BRskew],[CDNSskew],[CDWskew],[CSCOskew],[CTXSskew],[CTSHskew],[GLWskew],[DXCskew],[FFIVskew],[FISskew],[FISVskew],[FLTskew],[FLIRskew],[FTNTskew],[ITskew],[GPNskew],[HPEskew],[HPQskew],[INTCskew],[IBMskew],[INTUskew],[IPGPskew],[JKHYskew],[JNPRskew],[KEYSskew],[KLACskew],[LRCXskew],[LDOSskew],[MAskew],[MXIMskew],[MCHPskew],[MUskew],[MSFTskew],[MSIskew],[NTAPskew],[NLOKskew],[NVDAskew],[ORCLskew],[PAYXskew],[PAYCskew],[PYPLskew],[QRVOskew],[QCOMskew],[CRMskew],[STXskew],[NOWskew],[SWKSskew],[SNPSskew],[TELskew],[TXNskew],[VRSNskew],[Vskew],[WDCskew],[WUskew],[XRXskew],[XLNXskew],[ZBRAskew]],
        }

itdf = pd.DataFrame(Information_Technology, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','ACN','ADBE','AMD','AKAM','ADS','APH','ADI','ANSS','AAPL','AMAT','ANET','ADSK','ADP','AVGO','BR','CDNS','CDW','CSCO','CTXS','CTSH','GLW','DXC','FFIV','FIS','FISV','FLT','FLIR','FTNT','IT','GPN','HPE','HPQ','INTC','IBM','INTU','IPGP','JKHY','JNPR','KEYS','KLAC','LRCX','LDOS','MA','MXIM','MCHP','MU','MSFT','MSI','NTAP','NLOK','NVDA','ORCL','PAYX','PAYC','PYPL','QRVO','QCOM','CRM','STX','NOW','SWKS','SNPS','TEL','TXN','VRSN','V','WDC','WU','XRX','XLNX','ZBRA']
)

pd.DataFrame(itdf)
Out[580]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [0.001223507760026338] [0.0015945420321274149] [4.9155529057131195] [-0.00025923980184740865]
ACN [0.0008616586065586883] [0.0010492212716759122] [2.5211691517469665] [0.5704733720868931]
ADBE [0.0026947878004327562] [0.0012491830144232877] [7.066650769054527] [0.2595205557850686]
AMD [0.0031835873405016268] [0.0017431764656723548] [1.9811708074458991] [-0.23373025094070704]
... ... ... ... ...
WDC [0.001508273770848335] [0.0026361600505804264] [3.036622533322583] [-0.5454583948675107]
WU [-0.00013673031363339551] [0.0011060600768200298] [1.6226078941298265] [0.23136036870346707]
XRX [-0.004590064117318626] [0.0023316071805619645] [2.539668521519671] [-0.7237305284677074]
XLNX [0.0010889370445245998] [0.0011644203433454014] [1.7045654743571221] [0.25208435359231207]
ZBRA [0.001447869295884275] [0.0014397350221375263] [3.2538345597140577] [-0.17582490835919456]

73 rows × 4 columns

In [581]:
itdf.sort_values("Mean", ascending=False)
Out[581]:
Mean Variance Kurtosis Skew
NVDA [0.005174995635212475] [0.001873648834112013] [4.1715197510068975] [-0.26060509487954864]
PYPL [0.003874395222671166] [0.0014782177234444944] [5.013848158188924] [0.3743524698441213]
NOW [0.0035000477049536767] [0.0013792592281420906] [1.0426795250338108] [0.25137387979286485]
SWKS [0.0034930206186894385] [0.0019027333758710576] [3.886073363118588] [0.010029995802479508]
FTNT [0.003246560652088344] [0.0016998237307516767] [7.950539716553547] [0.9964743930711444]
... ... ... ... ...
NTAP [-0.0009583471270800894] [0.0014438715537028275] [2.1508963563771744] [0.313979938516237]
ADS [-0.0013081940948629074] [0.006125634728159878] [4.778357843383343] [0.510509848760287]
HPE [-0.001909411857475114] [0.001694208849197107] [3.2926739770860953] [-0.23837179708315517]
DXC [-0.0028400660280498155] [0.005706699743386464] [9.066405553025351] [1.3495278843448353]
XRX [-0.004590064117318626] [0.0023316071805619645] [2.539668521519671] [-0.7237305284677074]

73 rows × 4 columns

Communication Services

In [582]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ATVI = web.DataReader('ATVI', 'yahoo', start, end)
ATVI['returns'] = ATVI['Close'].pct_change(1)
ATVI['Cumulative Return'] = (1 + ATVI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ATVImean = ATVI['returns'].mean()
ATVIvar = ATVI['returns'].var()
ATVIkurt = ATVI['returns'].kurt()
ATVIskew = ATVI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ATVIbef = web.DataReader('ATVI', 'yahoo', start, crashstart)
ATVIbef['avgreturnbefore'] = ATVIbef['Close'].pct_change(1)
ATVIbef['Cumulative Return Before Crash'] = (1 + ATVIbef['avgreturnbefore']).cumprod()

#period of crash
ATVIdur = web.DataReader('ATVI', 'yahoo', crashstart, crashend)
ATVIdur['avgreturnduring'] = ATVIdur['Close'].pct_change(1)
ATVIdur['Cumulative Return During Crash'] = (1 + ATVIdur['avgreturnduring']).cumprod()

#period after crash
ATVIaft = web.DataReader('ATVI', 'yahoo', crashend, end)
ATVIaft['avgreturnafter'] = ATVIaft['Close'].pct_change(1)
ATVIaft['Cumulative Return After Crash'] = (1 + ATVIaft['avgreturnafter']).cumprod()
In [583]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GOOGL = web.DataReader('GOOGL', 'yahoo', start, end)
GOOGL['returns'] = GOOGL['Close'].pct_change(1)
GOOGL['Cumulative Return'] = (1 + GOOGL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GOOGLmean = GOOGL['returns'].mean()
GOOGLvar = GOOGL['returns'].var()
GOOGLkurt = GOOGL['returns'].kurt()
GOOGLskew = GOOGL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GOOGLbef = web.DataReader('GOOGL', 'yahoo', start, crashstart)
GOOGLbef['avgreturnbefore'] = GOOGLbef['Close'].pct_change(1)
GOOGLbef['Cumulative Return Before Crash'] = (1 + GOOGLbef['avgreturnbefore']).cumprod()

#period of crash
GOOGLdur = web.DataReader('GOOGL', 'yahoo', crashstart, crashend)
GOOGLdur['avgreturnduring'] = GOOGLdur['Close'].pct_change(1)
GOOGLdur['Cumulative Return During Crash'] = (1 + GOOGLdur['avgreturnduring']).cumprod()

#period after crash
GOOGLaft = web.DataReader('GOOGL', 'yahoo', crashend, end)
GOOGLaft['avgreturnafter'] = GOOGLaft['Close'].pct_change(1)
GOOGLaft['Cumulative Return After Crash'] = (1 + GOOGLaft['avgreturnafter']).cumprod()
In [584]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
GOOG = web.DataReader('GOOG', 'yahoo', start, end)
GOOG['returns'] = GOOG['Close'].pct_change(1)
GOOG['Cumulative Return'] = (1 + GOOG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
GOOGmean = GOOG['returns'].mean()
GOOGvar = GOOG['returns'].var()
GOOGkurt = GOOG['returns'].kurt()
GOOGskew = GOOG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
GOOGbef = web.DataReader('GOOG', 'yahoo', start, crashstart)
GOOGbef['avgreturnbefore'] = GOOGbef['Close'].pct_change(1)
GOOGbef['Cumulative Return Before Crash'] = (1 + GOOGbef['avgreturnbefore']).cumprod()

#period of crash
GOOGdur = web.DataReader('GOOG', 'yahoo', crashstart, crashend)
GOOGdur['avgreturnduring'] = GOOGdur['Close'].pct_change(1)
GOOGdur['Cumulative Return During Crash'] = (1 + GOOGdur['avgreturnduring']).cumprod()

#period after crash
GOOGaft = web.DataReader('GOOG', 'yahoo', crashend, end)
GOOGaft['avgreturnafter'] = GOOGaft['Close'].pct_change(1)
GOOGaft['Cumulative Return After Crash'] = (1 + GOOGaft['avgreturnafter']).cumprod()
In [585]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
T = web.DataReader('T', 'yahoo', start, end)
T['returns'] = T['Close'].pct_change(1)
T['Cumulative Return'] = (1 + T['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
Tmean = T['returns'].mean()
Tvar = T['returns'].var()
Tkurt = T['returns'].kurt()
Tskew = T['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
Tbef = web.DataReader('T', 'yahoo', start, crashstart)
Tbef['avgreturnbefore'] = Tbef['Close'].pct_change(1)
Tbef['Cumulative Return Before Crash'] = (1 + Tbef['avgreturnbefore']).cumprod()

#period of crash
Tdur = web.DataReader('T', 'yahoo', crashstart, crashend)
Tdur['avgreturnduring'] = Tdur['Close'].pct_change(1)
Tdur['Cumulative Return During Crash'] = (1 + Tdur['avgreturnduring']).cumprod()

#period after crash
Taft = web.DataReader('T', 'yahoo', crashend, end)
Taft['avgreturnafter'] = Taft['Close'].pct_change(1)
Taft['Cumulative Return After Crash'] = (1 + Taft['avgreturnafter']).cumprod()
In [586]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CTL = web.DataReader('CTL', 'yahoo', start, end)
CTL['returns'] = CTL['Close'].pct_change(1)
CTL['Cumulative Return'] = (1 + CTL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CTLmean = CTL['returns'].mean()
CTLvar = CTL['returns'].var()
CTLkurt = CTL['returns'].kurt()
CTLskew = CTL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CTLbef = web.DataReader('CTL', 'yahoo', start, crashstart)
CTLbef['avgreturnbefore'] = CTLbef['Close'].pct_change(1)
CTLbef['Cumulative Return Before Crash'] = (1 + CTLbef['avgreturnbefore']).cumprod()

#period of crash
CTLdur = web.DataReader('CTL', 'yahoo', crashstart, crashend)
CTLdur['avgreturnduring'] = CTLdur['Close'].pct_change(1)
CTLdur['Cumulative Return During Crash'] = (1 + CTLdur['avgreturnduring']).cumprod()

#period after crash
CTLaft = web.DataReader('CTL', 'yahoo', crashend, end)
CTLaft['avgreturnafter'] = CTLaft['Close'].pct_change(1)
CTLaft['Cumulative Return After Crash'] = (1 + CTLaft['avgreturnafter']).cumprod()
In [587]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CHTR = web.DataReader('CHTR', 'yahoo', start, end)
CHTR['returns'] = CHTR['Close'].pct_change(1)
CHTR['Cumulative Return'] = (1 + CHTR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CHTRmean = CHTR['returns'].mean()
CHTRvar = CHTR['returns'].var()
CHTRkurt = CHTR['returns'].kurt()
CHTRskew = CHTR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CHTRbef = web.DataReader('CHTR', 'yahoo', start, crashstart)
CHTRbef['avgreturnbefore'] = CHTRbef['Close'].pct_change(1)
CHTRbef['Cumulative Return Before Crash'] = (1 + CHTRbef['avgreturnbefore']).cumprod()

#period of crash
CHTRdur = web.DataReader('CHTR', 'yahoo', crashstart, crashend)
CHTRdur['avgreturnduring'] = CHTRdur['Close'].pct_change(1)
CHTRdur['Cumulative Return During Crash'] = (1 + CHTRdur['avgreturnduring']).cumprod()

#period after crash
CHTRaft = web.DataReader('CHTR', 'yahoo', crashend, end)
CHTRaft['avgreturnafter'] = CHTRaft['Close'].pct_change(1)
CHTRaft['Cumulative Return After Crash'] = (1 + CHTRaft['avgreturnafter']).cumprod()
In [588]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CMCSA = web.DataReader('CMCSA', 'yahoo', start, end)
CMCSA['returns'] = CMCSA['Close'].pct_change(1)
CMCSA['Cumulative Return'] = (1 + CMCSA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CMCSAmean = CMCSA['returns'].mean()
CMCSAvar = CMCSA['returns'].var()
CMCSAkurt = CMCSA['returns'].kurt()
CMCSAskew = CMCSA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CMCSAbef = web.DataReader('CMCSA', 'yahoo', start, crashstart)
CMCSAbef['avgreturnbefore'] = CMCSAbef['Close'].pct_change(1)
CMCSAbef['Cumulative Return Before Crash'] = (1 + CMCSAbef['avgreturnbefore']).cumprod()

#period of crash
CMCSAdur = web.DataReader('CMCSA', 'yahoo', crashstart, crashend)
CMCSAdur['avgreturnduring'] = CMCSAdur['Close'].pct_change(1)
CMCSAdur['Cumulative Return During Crash'] = (1 + CMCSAdur['avgreturnduring']).cumprod()

#period after crash
CMCSAaft = web.DataReader('CMCSA', 'yahoo', crashend, end)
CMCSAaft['avgreturnafter'] = CMCSAaft['Close'].pct_change(1)
CMCSAaft['Cumulative Return After Crash'] = (1 + CMCSAaft['avgreturnafter']).cumprod()
In [589]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DISCA = web.DataReader('DISCA', 'yahoo', start, end)
DISCA['returns'] = DISCA['Close'].pct_change(1)
DISCA['Cumulative Return'] = (1 + DISCA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DISCAmean = DISCA['returns'].mean()
DISCAvar = DISCA['returns'].var()
DISCAkurt = DISCA['returns'].kurt()
DISCAskew = DISCA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DISCAbef = web.DataReader('DISCA', 'yahoo', start, crashstart)
DISCAbef['avgreturnbefore'] = DISCAbef['Close'].pct_change(1)
DISCAbef['Cumulative Return Before Crash'] = (1 + DISCAbef['avgreturnbefore']).cumprod()

#period of crash
DISCAdur = web.DataReader('DISCA', 'yahoo', crashstart, crashend)
DISCAdur['avgreturnduring'] = DISCAdur['Close'].pct_change(1)
DISCAdur['Cumulative Return During Crash'] = (1 + DISCAdur['avgreturnduring']).cumprod()

#period after crash
DISCAaft = web.DataReader('DISCA', 'yahoo', crashend, end)
DISCAaft['avgreturnafter'] = DISCAaft['Close'].pct_change(1)
DISCAaft['Cumulative Return After Crash'] = (1 + DISCAaft['avgreturnafter']).cumprod()
In [590]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DISCK = web.DataReader('DISCK', 'yahoo', start, end)
DISCK['returns'] = DISCK['Close'].pct_change(1)
DISCK['Cumulative Return'] = (1 + DISCK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DISCKmean = DISCK['returns'].mean()
DISCKvar = DISCK['returns'].var()
DISCKkurt = DISCK['returns'].kurt()
DISCKskew = DISCK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DISCKbef = web.DataReader('DISCK', 'yahoo', start, crashstart)
DISCKbef['avgreturnbefore'] = DISCKbef['Close'].pct_change(1)
DISCKbef['Cumulative Return Before Crash'] = (1 + DISCKbef['avgreturnbefore']).cumprod()

#period of crash
DISCKdur = web.DataReader('DISCK', 'yahoo', crashstart, crashend)
DISCKdur['avgreturnduring'] = DISCKdur['Close'].pct_change(1)
DISCKdur['Cumulative Return During Crash'] = (1 + DISCKdur['avgreturnduring']).cumprod()

#period after crash
DISCKaft = web.DataReader('DISCK', 'yahoo', crashend, end)
DISCKaft['avgreturnafter'] = DISCKaft['Close'].pct_change(1)
DISCKaft['Cumulative Return After Crash'] = (1 + DISCKaft['avgreturnafter']).cumprod()
In [591]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DISH = web.DataReader('DISH', 'yahoo', start, end)
DISH['returns'] = DISH['Close'].pct_change(1)
DISH['Cumulative Return'] = (1 + DISH['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DISHmean = DISH['returns'].mean()
DISHvar = DISH['returns'].var()
DISHkurt = DISH['returns'].kurt()
DISHskew = DISH['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DISHbef = web.DataReader('DISH', 'yahoo', start, crashstart)
DISHbef['avgreturnbefore'] = DISHbef['Close'].pct_change(1)
DISHbef['Cumulative Return Before Crash'] = (1 + DISHbef['avgreturnbefore']).cumprod()

#period of crash
DISHdur = web.DataReader('DISH', 'yahoo', crashstart, crashend)
DISHdur['avgreturnduring'] = DISHdur['Close'].pct_change(1)
DISHdur['Cumulative Return During Crash'] = (1 + DISHdur['avgreturnduring']).cumprod()

#period after crash
DISHaft = web.DataReader('DISH', 'yahoo', crashend, end)
DISHaft['avgreturnafter'] = DISHaft['Close'].pct_change(1)
DISHaft['Cumulative Return After Crash'] = (1 + DISHaft['avgreturnafter']).cumprod()
In [592]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EA = web.DataReader('EA', 'yahoo', start, end)
EA['returns'] = EA['Close'].pct_change(1)
EA['Cumulative Return'] = (1 + EA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EAmean = EA['returns'].mean()
EAvar = EA['returns'].var()
EAkurt = EA['returns'].kurt()
EAskew = EA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EAbef = web.DataReader('EA', 'yahoo', start, crashstart)
EAbef['avgreturnbefore'] = EAbef['Close'].pct_change(1)
EAbef['Cumulative Return Before Crash'] = (1 + EAbef['avgreturnbefore']).cumprod()

#period of crash
EAdur = web.DataReader('EA', 'yahoo', crashstart, crashend)
EAdur['avgreturnduring'] = EAdur['Close'].pct_change(1)
EAdur['Cumulative Return During Crash'] = (1 + EAdur['avgreturnduring']).cumprod()

#period after crash
EAaft = web.DataReader('EA', 'yahoo', crashend, end)
EAaft['avgreturnafter'] = EAaft['Close'].pct_change(1)
EAaft['Cumulative Return After Crash'] = (1 + EAaft['avgreturnafter']).cumprod()
In [593]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FB = web.DataReader('FB', 'yahoo', start, end)
FB['returns'] = FB['Close'].pct_change(1)
FB['Cumulative Return'] = (1 + FB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FBmean = FB['returns'].mean()
FBvar = FB['returns'].var()
FBkurt = FB['returns'].kurt()
FBskew = FB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FBbef = web.DataReader('FB', 'yahoo', start, crashstart)
FBbef['avgreturnbefore'] = FBbef['Close'].pct_change(1)
FBbef['Cumulative Return Before Crash'] = (1 + FBbef['avgreturnbefore']).cumprod()

#period of crash
FBdur = web.DataReader('FB', 'yahoo', crashstart, crashend)
FBdur['avgreturnduring'] = FBdur['Close'].pct_change(1)
FBdur['Cumulative Return During Crash'] = (1 + FBdur['avgreturnduring']).cumprod()

#period after crash
FBaft = web.DataReader('FB', 'yahoo', crashend, end)
FBaft['avgreturnafter'] = FBaft['Close'].pct_change(1)
FBaft['Cumulative Return After Crash'] = (1 + FBaft['avgreturnafter']).cumprod()
In [594]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FOXA = web.DataReader('FOXA', 'yahoo', start, end)
FOXA['returns'] = FOXA['Close'].pct_change(1)
FOXA['Cumulative Return'] = (1 + FOXA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FOXAmean = FOXA['returns'].mean()
FOXAvar = FOXA['returns'].var()
FOXAkurt = FOXA['returns'].kurt()
FOXAskew = FOXA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FOXAbef = web.DataReader('FOXA', 'yahoo', start, crashstart)
FOXAbef['avgreturnbefore'] = FOXAbef['Close'].pct_change(1)
FOXAbef['Cumulative Return Before Crash'] = (1 + FOXAbef['avgreturnbefore']).cumprod()

#period of crash
FOXAdur = web.DataReader('FOXA', 'yahoo', crashstart, crashend)
FOXAdur['avgreturnduring'] = FOXAdur['Close'].pct_change(1)
FOXAdur['Cumulative Return During Crash'] = (1 + FOXAdur['avgreturnduring']).cumprod()

#period after crash
FOXAaft = web.DataReader('FOXA', 'yahoo', crashend, end)
FOXAaft['avgreturnafter'] = FOXAaft['Close'].pct_change(1)
FOXAaft['Cumulative Return After Crash'] = (1 + FOXAaft['avgreturnafter']).cumprod()
In [595]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FOX = web.DataReader('FOX', 'yahoo', start, end)
FOX['returns'] = FOX['Close'].pct_change(1)
FOX['Cumulative Return'] = (1 + FOX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FOXmean = FOX['returns'].mean()
FOXvar = FOX['returns'].var()
FOXkurt = FOX['returns'].kurt()
FOXskew = FOX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FOXbef = web.DataReader('FOX', 'yahoo', start, crashstart)
FOXbef['avgreturnbefore'] = FOXbef['Close'].pct_change(1)
FOXbef['Cumulative Return Before Crash'] = (1 + FOXbef['avgreturnbefore']).cumprod()

#period of crash
FOXdur = web.DataReader('FOX', 'yahoo', crashstart, crashend)
FOXdur['avgreturnduring'] = FOXdur['Close'].pct_change(1)
FOXdur['Cumulative Return During Crash'] = (1 + FOXdur['avgreturnduring']).cumprod()

#period after crash
FOXaft = web.DataReader('FOX', 'yahoo', crashend, end)
FOXaft['avgreturnafter'] = FOXaft['Close'].pct_change(1)
FOXaft['Cumulative Return After Crash'] = (1 + FOXaft['avgreturnafter']).cumprod()
In [596]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IPG = web.DataReader('IPG', 'yahoo', start, end)
IPG['returns'] = IPG['Close'].pct_change(1)
IPG['Cumulative Return'] = (1 + IPG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IPGmean = IPG['returns'].mean()
IPGvar = IPG['returns'].var()
IPGkurt = IPG['returns'].kurt()
IPGskew = IPG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IPGbef = web.DataReader('IPG', 'yahoo', start, crashstart)
IPGbef['avgreturnbefore'] = IPGbef['Close'].pct_change(1)
IPGbef['Cumulative Return Before Crash'] = (1 + IPGbef['avgreturnbefore']).cumprod()

#period of crash
IPGdur = web.DataReader('IPG', 'yahoo', crashstart, crashend)
IPGdur['avgreturnduring'] = IPGdur['Close'].pct_change(1)
IPGdur['Cumulative Return During Crash'] = (1 + IPGdur['avgreturnduring']).cumprod()

#period after crash
IPGaft = web.DataReader('IPG', 'yahoo', crashend, end)
IPGaft['avgreturnafter'] = IPGaft['Close'].pct_change(1)
IPGaft['Cumulative Return After Crash'] = (1 + IPGaft['avgreturnafter']).cumprod()
In [597]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LYV = web.DataReader('LYV', 'yahoo', start, end)
LYV['returns'] = LYV['Close'].pct_change(1)
LYV['Cumulative Return'] = (1 + LYV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LYVmean = LYV['returns'].mean()
LYVvar = LYV['returns'].var()
LYVkurt = LYV['returns'].kurt()
LYVskew = LYV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LYVbef = web.DataReader('LYV', 'yahoo', start, crashstart)
LYVbef['avgreturnbefore'] = LYVbef['Close'].pct_change(1)
LYVbef['Cumulative Return Before Crash'] = (1 + LYVbef['avgreturnbefore']).cumprod()

#period of crash
LYVdur = web.DataReader('LYV', 'yahoo', crashstart, crashend)
LYVdur['avgreturnduring'] = LYVdur['Close'].pct_change(1)
LYVdur['Cumulative Return During Crash'] = (1 + LYVdur['avgreturnduring']).cumprod()

#period after crash
LYVaft = web.DataReader('LYV', 'yahoo', crashend, end)
LYVaft['avgreturnafter'] = LYVaft['Close'].pct_change(1)
LYVaft['Cumulative Return After Crash'] = (1 + LYVaft['avgreturnafter']).cumprod()
In [600]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NFLX = web.DataReader('NFLX', 'yahoo', start, end)
NFLX['returns'] = NFLX['Close'].pct_change(1)
NFLX['Cumulative Return'] = (1 + NFLX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NFLXmean = NFLX['returns'].mean()
NFLXvar = NFLX['returns'].var()
NFLXkurt = NFLX['returns'].kurt()
NFLXskew = NFLX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NFLXbef = web.DataReader('NFLX', 'yahoo', start, crashstart)
NFLXbef['avgreturnbefore'] = NFLXbef['Close'].pct_change(1)
NFLXbef['Cumulative Return Before Crash'] = (1 + NFLXbef['avgreturnbefore']).cumprod()

#period of crash
NFLXdur = web.DataReader('NFLX', 'yahoo', crashstart, crashend)
NFLXdur['avgreturnduring'] = NFLXdur['Close'].pct_change(1)
NFLXdur['Cumulative Return During Crash'] = (1 + NFLXdur['avgreturnduring']).cumprod()

#period after crash
NFLXaft = web.DataReader('NFLX', 'yahoo', crashend, end)
NFLXaft['avgreturnafter'] = NFLXaft['Close'].pct_change(1)
NFLXaft['Cumulative Return After Crash'] = (1 + NFLXaft['avgreturnafter']).cumprod()
In [601]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NWSA = web.DataReader('NWSA', 'yahoo', start, end)
NWSA['returns'] = NWSA['Close'].pct_change(1)
NWSA['Cumulative Return'] = (1 + NWSA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NWSAmean = NWSA['returns'].mean()
NWSAvar = NWSA['returns'].var()
NWSAkurt = NWSA['returns'].kurt()
NWSAskew = NWSA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NWSAbef = web.DataReader('NWSA', 'yahoo', start, crashstart)
NWSAbef['avgreturnbefore'] = NWSAbef['Close'].pct_change(1)
NWSAbef['Cumulative Return Before Crash'] = (1 + NWSAbef['avgreturnbefore']).cumprod()

#period of crash
NWSAdur = web.DataReader('NWSA', 'yahoo', crashstart, crashend)
NWSAdur['avgreturnduring'] = NWSAdur['Close'].pct_change(1)
NWSAdur['Cumulative Return During Crash'] = (1 + NWSAdur['avgreturnduring']).cumprod()

#period after crash
NWSAaft = web.DataReader('NWSA', 'yahoo', crashend, end)
NWSAaft['avgreturnafter'] = NWSAaft['Close'].pct_change(1)
NWSAaft['Cumulative Return After Crash'] = (1 + NWSAaft['avgreturnafter']).cumprod()
In [602]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NWS = web.DataReader('NWS', 'yahoo', start, end)
NWS['returns'] = NWS['Close'].pct_change(1)
NWS['Cumulative Return'] = (1 + NWS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NWSmean = NWS['returns'].mean()
NWSvar = NWS['returns'].var()
NWSkurt = NWS['returns'].kurt()
NWSskew = NWS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NWSbef = web.DataReader('NWS', 'yahoo', start, crashstart)
NWSbef['avgreturnbefore'] = NWSbef['Close'].pct_change(1)
NWSbef['Cumulative Return Before Crash'] = (1 + NWSbef['avgreturnbefore']).cumprod()

#period of crash
NWSdur = web.DataReader('NWS', 'yahoo', crashstart, crashend)
NWSdur['avgreturnduring'] = NWSdur['Close'].pct_change(1)
NWSdur['Cumulative Return During Crash'] = (1 + NWSdur['avgreturnduring']).cumprod()

#period after crash
NWSaft = web.DataReader('NWS', 'yahoo', crashend, end)
NWSaft['avgreturnafter'] = NWSaft['Close'].pct_change(1)
NWSaft['Cumulative Return After Crash'] = (1 + NWSaft['avgreturnafter']).cumprod()
In [603]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
OMC = web.DataReader('OMC', 'yahoo', start, end)
OMC['returns'] = OMC['Close'].pct_change(1)
OMC['Cumulative Return'] = (1 + OMC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
OMCmean = OMC['returns'].mean()
OMCvar = OMC['returns'].var()
OMCkurt = OMC['returns'].kurt()
OMCskew = OMC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
OMCbef = web.DataReader('OMC', 'yahoo', start, crashstart)
OMCbef['avgreturnbefore'] = OMCbef['Close'].pct_change(1)
OMCbef['Cumulative Return Before Crash'] = (1 + OMCbef['avgreturnbefore']).cumprod()

#period of crash
OMCdur = web.DataReader('OMC', 'yahoo', crashstart, crashend)
OMCdur['avgreturnduring'] = OMCdur['Close'].pct_change(1)
OMCdur['Cumulative Return During Crash'] = (1 + OMCdur['avgreturnduring']).cumprod()

#period after crash
OMCaft = web.DataReader('OMC', 'yahoo', crashend, end)
OMCaft['avgreturnafter'] = OMCaft['Close'].pct_change(1)
OMCaft['Cumulative Return After Crash'] = (1 + OMCaft['avgreturnafter']).cumprod()
In [604]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TMUS = web.DataReader('TMUS', 'yahoo', start, end)
TMUS['returns'] = TMUS['Close'].pct_change(1)
TMUS['Cumulative Return'] = (1 + TMUS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TMUSmean = TMUS['returns'].mean()
TMUSvar = TMUS['returns'].var()
TMUSkurt = TMUS['returns'].kurt()
TMUSskew = TMUS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TMUSbef = web.DataReader('TMUS', 'yahoo', start, crashstart)
TMUSbef['avgreturnbefore'] = TMUSbef['Close'].pct_change(1)
TMUSbef['Cumulative Return Before Crash'] = (1 + TMUSbef['avgreturnbefore']).cumprod()

#period of crash
TMUSdur = web.DataReader('TMUS', 'yahoo', crashstart, crashend)
TMUSdur['avgreturnduring'] = TMUSdur['Close'].pct_change(1)
TMUSdur['Cumulative Return During Crash'] = (1 + TMUSdur['avgreturnduring']).cumprod()

#period after crash
TMUSaft = web.DataReader('TMUS', 'yahoo', crashend, end)
TMUSaft['avgreturnafter'] = TMUSaft['Close'].pct_change(1)
TMUSaft['Cumulative Return After Crash'] = (1 + TMUSaft['avgreturnafter']).cumprod()
In [605]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TTWO = web.DataReader('TTWO', 'yahoo', start, end)
TTWO['returns'] = TTWO['Close'].pct_change(1)
TTWO['Cumulative Return'] = (1 + TTWO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TTWOmean = TTWO['returns'].mean()
TTWOvar = TTWO['returns'].var()
TTWOkurt = TTWO['returns'].kurt()
TTWOskew = TTWO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TTWObef = web.DataReader('TTWO', 'yahoo', start, crashstart)
TTWObef['avgreturnbefore'] = TTWObef['Close'].pct_change(1)
TTWObef['Cumulative Return Before Crash'] = (1 + TTWObef['avgreturnbefore']).cumprod()

#period of crash
TTWOdur = web.DataReader('TTWO', 'yahoo', crashstart, crashend)
TTWOdur['avgreturnduring'] = TTWOdur['Close'].pct_change(1)
TTWOdur['Cumulative Return During Crash'] = (1 + TTWOdur['avgreturnduring']).cumprod()

#period after crash
TTWOaft = web.DataReader('TTWO', 'yahoo', crashend, end)
TTWOaft['avgreturnafter'] = TTWOaft['Close'].pct_change(1)
TTWOaft['Cumulative Return After Crash'] = (1 + TTWOaft['avgreturnafter']).cumprod()
In [606]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
TWTR = web.DataReader('TWTR', 'yahoo', start, end)
TWTR['returns'] = TWTR['Close'].pct_change(1)
TWTR['Cumulative Return'] = (1 + TWTR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
TWTRmean = TWTR['returns'].mean()
TWTRvar = TWTR['returns'].var()
TWTRkurt = TWTR['returns'].kurt()
TWTRskew = TWTR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
TWTRbef = web.DataReader('TWTR', 'yahoo', start, crashstart)
TWTRbef['avgreturnbefore'] = TWTRbef['Close'].pct_change(1)
TWTRbef['Cumulative Return Before Crash'] = (1 + TWTRbef['avgreturnbefore']).cumprod()

#period of crash
TWTRdur = web.DataReader('TWTR', 'yahoo', crashstart, crashend)
TWTRdur['avgreturnduring'] = TWTRdur['Close'].pct_change(1)
TWTRdur['Cumulative Return During Crash'] = (1 + TWTRdur['avgreturnduring']).cumprod()

#period after crash
TWTRaft = web.DataReader('TWTR', 'yahoo', crashend, end)
TWTRaft['avgreturnafter'] = TWTRaft['Close'].pct_change(1)
TWTRaft['Cumulative Return After Crash'] = (1 + TWTRaft['avgreturnafter']).cumprod()
In [607]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
VZ = web.DataReader('VZ', 'yahoo', start, end)
VZ['returns'] = VZ['Close'].pct_change(1)
VZ['Cumulative Return'] = (1 + VZ['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
VZmean = VZ['returns'].mean()
VZvar = VZ['returns'].var()
VZkurt = VZ['returns'].kurt()
VZskew = VZ['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
VZbef = web.DataReader('VZ', 'yahoo', start, crashstart)
VZbef['avgreturnbefore'] = VZbef['Close'].pct_change(1)
VZbef['Cumulative Return Before Crash'] = (1 + VZbef['avgreturnbefore']).cumprod()

#period of crash
VZdur = web.DataReader('VZ', 'yahoo', crashstart, crashend)
VZdur['avgreturnduring'] = VZdur['Close'].pct_change(1)
VZdur['Cumulative Return During Crash'] = (1 + VZdur['avgreturnduring']).cumprod()

#period after crash
VZaft = web.DataReader('VZ', 'yahoo', crashend, end)
VZaft['avgreturnafter'] = VZaft['Close'].pct_change(1)
VZaft['Cumulative Return After Crash'] = (1 + VZaft['avgreturnafter']).cumprod()
In [608]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
VIAC = web.DataReader('VIAC', 'yahoo', start, end)
VIAC['returns'] = VIAC['Close'].pct_change(1)
VIAC['Cumulative Return'] = (1 + VIAC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
VIACmean = VIAC['returns'].mean()
VIACvar = VIAC['returns'].var()
VIACkurt = VIAC['returns'].kurt()
VIACskew = VIAC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
VIACbef = web.DataReader('VIAC', 'yahoo', start, crashstart)
VIACbef['avgreturnbefore'] = VIACbef['Close'].pct_change(1)
VIACbef['Cumulative Return Before Crash'] = (1 + VIACbef['avgreturnbefore']).cumprod()

#period of crash
VIACdur = web.DataReader('VIAC', 'yahoo', crashstart, crashend)
VIACdur['avgreturnduring'] = VIACdur['Close'].pct_change(1)
VIACdur['Cumulative Return During Crash'] = (1 + VIACdur['avgreturnduring']).cumprod()

#period after crash
VIACaft = web.DataReader('VIAC', 'yahoo', crashend, end)
VIACaft['avgreturnafter'] = VIACaft['Close'].pct_change(1)
VIACaft['Cumulative Return After Crash'] = (1 + VIACaft['avgreturnafter']).cumprod()
In [609]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DIS = web.DataReader('DIS', 'yahoo', start, end)
DIS['returns'] = DIS['Close'].pct_change(1)
DIS['Cumulative Return'] = (1 + DIS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DISmean = DIS['returns'].mean()
DISvar = DIS['returns'].var()
DISkurt = DIS['returns'].kurt()
DISskew = DIS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DISbef = web.DataReader('DIS', 'yahoo', start, crashstart)
DISbef['avgreturnbefore'] = DISbef['Close'].pct_change(1)
DISbef['Cumulative Return Before Crash'] = (1 + DISbef['avgreturnbefore']).cumprod()

#period of crash
DISdur = web.DataReader('DIS', 'yahoo', crashstart, crashend)
DISdur['avgreturnduring'] = DISdur['Close'].pct_change(1)
DISdur['Cumulative Return During Crash'] = (1 + DISdur['avgreturnduring']).cumprod()

#period after crash
DISaft = web.DataReader('DIS', 'yahoo', crashend, end)
DISaft['avgreturnafter'] = DISaft['Close'].pct_change(1)
DISaft['Cumulative Return After Crash'] = (1 + DISaft['avgreturnafter']).cumprod()
In [610]:
#KDE Plot for Communication Services Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
ATVI['returns'].plot(kind = 'kde', label ='ATVI', figsize = (10,8), alpha=0.5, title = 'KDE of Communication Services Stocks')
GOOGL['returns'].plot(kind = 'kde', label ='GOOGL', alpha=0.5)
GOOG['returns'].plot(kind = 'kde', label ='GOOG', alpha=0.5)
T['returns'].plot(kind = 'kde', label ='T', alpha=0.5)
CTL['returns'].plot(kind = 'kde', label ='CTL', alpha=0.5)
CHTR['returns'].plot(kind = 'kde', label ='CHTR', alpha=0.5)
CMCSA['returns'].plot(kind = 'kde', label ='CMCSA', alpha=0.5)
DISCA['returns'].plot(kind = 'kde', label ='DISCA', alpha=0.5)
DISCK['returns'].plot(kind = 'kde', label ='DISCK', alpha=0.5)
DISH['returns'].plot(kind = 'kde', label ='DISH', alpha=0.5)
EA['returns'].plot(kind = 'kde', label ='EA', alpha=0.5)
FB['returns'].plot(kind = 'kde', label ='FB', alpha=0.5)
FOXA['returns'].plot(kind = 'kde', label ='FOXA', alpha=0.5)
FOX['returns'].plot(kind = 'kde', label ='FOX', alpha=0.5)
IPG['returns'].plot(kind = 'kde', label = 'IPG', alpha=0.5)
LYV['returns'].plot(kind = 'kde', label ='LYV', alpha=0.5)
NFLX['returns'].plot(kind = 'kde', label ='NFLX', alpha=0.5)
NWSA['returns'].plot(kind = 'kde', label ='NWSA', alpha=0.5)
NWS['returns'].plot(kind = 'kde', label ='NWS', alpha=0.5)
OMC['returns'].plot(kind = 'kde', label ='OMC', alpha=0.5)
TMUS['returns'].plot(kind = 'kde', label ='TMUS', alpha=0.5)
TTWO['returns'].plot(kind = 'kde', label ='TTWO', alpha=0.5)
TWTR['returns'].plot(kind = 'kde', label ='TWTR', alpha=0.5)
VZ['returns'].plot(kind = 'kde', label ='VZ', alpha=0.5)
VIAC['returns'].plot(kind = 'kde', label ='VIAC', alpha=0.5)
DIS['returns'].plot(kind = 'kde', label ='DIS', alpha=0.5)

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [611]:
#Boxplots comparing returns for Communication Services Sector
box_df = pd.concat([sp['returns'],ATVI['returns'],GOOGL['returns'],GOOG['returns'],T['returns'],CTL['returns'],CHTR['returns'],CMCSA['returns'],DISCA['returns'],DISCK['returns'],DISH['returns'],EA['returns'],FB['returns'],FOXA['returns'],FOX['returns'],IPG['returns'],LYV['returns'],NFLX['returns'],NWSA['returns'],NWS['returns'],OMC['returns'],TMUS['returns'],TTWO['returns'],TWTR['returns'],VZ['returns'],VIAC['returns'],DIS['returns']], axis=1)
box_df.columns = ['S&P 500','ATVI','GOOGL','GOOG','T','CTL','CHTR','CMCSA','DISCA','DISCK','DISH','EA','FB','FOXA','FOX','IPG','LYV','NFLX','NWSA','NWS','OMC','TMUS','TTWO','TWTR','VZ','VIAC','DIS']
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Communication Services Sector Returns')
Out[611]:
<matplotlib.axes._subplots.AxesSubplot at 0x13fa57e1c50>
In [612]:
#Cumulative Returns of Communication Services Stocks for Whole period

sp['Cumulative Return'].plot(label="S&P 500")
ATVI['Cumulative Return'].plot(label='ATVI', figsize=(16,8), title = 'Cumulative Return of Communication Services Stocks')
GOOGL['Cumulative Return'].plot(label="GOOGL")
GOOG['Cumulative Return'].plot(label="GOOG")
T['Cumulative Return'].plot(label="T")
CTL['Cumulative Return'].plot(label="CTL")
CHTR['Cumulative Return'].plot(label="CHTR")
CMCSA['Cumulative Return'].plot(label="CMCSA")
DISCA['Cumulative Return'].plot(label="DISCA")
DISCK['Cumulative Return'].plot(label="DISCK")
DISH['Cumulative Return'].plot(label="DISH")
EA['Cumulative Return'].plot(label="EA")
FB['Cumulative Return'].plot(label="FB")
FOXA['Cumulative Return'].plot(label="FOXA")
FOX['Cumulative Return'].plot(label="FOX")
IPG['Cumulative Return'].plot(label="IPG")
LYV['Cumulative Return'].plot(label="LYV")
NFLX['Cumulative Return'].plot(label="NFLX")
NWSA['Cumulative Return'].plot(label="NWSA")
NWS['Cumulative Return'].plot(label="NWS")
OMC['Cumulative Return'].plot(label="OMC")
TMUS['Cumulative Return'].plot(label="TMUS")
TTWO['Cumulative Return'].plot(label="TTWO")
TWTR['Cumulative Return'].plot(label="TWTR")
VZ['Cumulative Return'].plot(label="VZ")
VIAC['Cumulative Return'].plot(label="VIAC")
DIS['Cumulative Return'].plot(label="DIS")

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [613]:
#Sorting by Cumulative Return

cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],ATVI['Cumulative Return'].iloc[-1],GOOGL['Cumulative Return'].iloc[-1],GOOG['Cumulative Return'].iloc[-1],T['Cumulative Return'].iloc[-1],CTL['Cumulative Return'].iloc[-1],CHTR['Cumulative Return'].iloc[-1],CMCSA['Cumulative Return'].iloc[-1],DISCA['Cumulative Return'].iloc[-1],DISCK['Cumulative Return'].iloc[-1],DISH['Cumulative Return'].iloc[-1],EA['Cumulative Return'].iloc[-1],FB['Cumulative Return'].iloc[-1],FOXA['Cumulative Return'].iloc[-1],FOX['Cumulative Return'].iloc[-1],IPG['Cumulative Return'].iloc[-1],LYV['Cumulative Return'].iloc[-1],NFLX['Cumulative Return'].iloc[-1],NWSA['Cumulative Return'].iloc[-1],NWS['Cumulative Return'].iloc[-1],OMC['Cumulative Return'].iloc[-1],TMUS['Cumulative Return'].iloc[-1],TTWO['Cumulative Return'].iloc[-1],TWTR['Cumulative Return'].iloc[-1],VZ['Cumulative Return'].iloc[-1],VIAC['Cumulative Return'].iloc[-1],DIS['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],ATVIbef['Cumulative Return Before Crash'].iloc[-1],GOOGLbef['Cumulative Return Before Crash'].iloc[-1],GOOGbef['Cumulative Return Before Crash'].iloc[-1],Tbef['Cumulative Return Before Crash'].iloc[-1],CTLbef['Cumulative Return Before Crash'].iloc[-1],CHTRbef['Cumulative Return Before Crash'].iloc[-1],CMCSAbef['Cumulative Return Before Crash'].iloc[-1],DISCAbef['Cumulative Return Before Crash'].iloc[-1],DISCKbef['Cumulative Return Before Crash'].iloc[-1],DISHbef['Cumulative Return Before Crash'].iloc[-1],EAbef['Cumulative Return Before Crash'].iloc[-1],FBbef['Cumulative Return Before Crash'].iloc[-1],FOXAbef['Cumulative Return Before Crash'].iloc[-1],FOXbef['Cumulative Return Before Crash'].iloc[-1],IPGbef['Cumulative Return Before Crash'].iloc[-1],LYVbef['Cumulative Return Before Crash'].iloc[-1],NFLXbef['Cumulative Return Before Crash'].iloc[-1],NWSAbef['Cumulative Return Before Crash'].iloc[-1],NWSbef['Cumulative Return Before Crash'].iloc[-1],OMCbef['Cumulative Return Before Crash'].iloc[-1],TMUSbef['Cumulative Return Before Crash'].iloc[-1],TTWObef['Cumulative Return Before Crash'].iloc[-1],TWTRbef['Cumulative Return Before Crash'].iloc[-1],VZbef['Cumulative Return Before Crash'].iloc[-1],VIACbef['Cumulative Return Before Crash'].iloc[-1],DISbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],ATVIdur['Cumulative Return During Crash'].iloc[-1],GOOGLdur['Cumulative Return During Crash'].iloc[-1],GOOGdur['Cumulative Return During Crash'].iloc[-1],Tdur['Cumulative Return During Crash'].iloc[-1],CTLdur['Cumulative Return During Crash'].iloc[-1],CHTRdur['Cumulative Return During Crash'].iloc[-1],CMCSAdur['Cumulative Return During Crash'].iloc[-1],DISCAdur['Cumulative Return During Crash'].iloc[-1],DISCKdur['Cumulative Return During Crash'].iloc[-1],DISHdur['Cumulative Return During Crash'].iloc[-1],EAdur['Cumulative Return During Crash'].iloc[-1],FBdur['Cumulative Return During Crash'].iloc[-1],FOXAdur['Cumulative Return During Crash'].iloc[-1],FOXdur['Cumulative Return During Crash'].iloc[-1],IPGdur['Cumulative Return During Crash'].iloc[-1],LYVdur['Cumulative Return During Crash'].iloc[-1],NFLXdur['Cumulative Return During Crash'].iloc[-1],NWSAdur['Cumulative Return During Crash'].iloc[-1],NWSdur['Cumulative Return During Crash'].iloc[-1],OMCdur['Cumulative Return During Crash'].iloc[-1],TMUSdur['Cumulative Return During Crash'].iloc[-1],TTWOdur['Cumulative Return During Crash'].iloc[-1],TWTRdur['Cumulative Return During Crash'].iloc[-1],VZdur['Cumulative Return During Crash'].iloc[-1],VIACdur['Cumulative Return During Crash'].iloc[-1],DISdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],ATVIaft['Cumulative Return After Crash'].iloc[-1],GOOGLaft['Cumulative Return After Crash'].iloc[-1],GOOGaft['Cumulative Return After Crash'].iloc[-1],Taft['Cumulative Return After Crash'].iloc[-1],CTLaft['Cumulative Return After Crash'].iloc[-1],CHTRaft['Cumulative Return After Crash'].iloc[-1],CMCSAaft['Cumulative Return After Crash'].iloc[-1],DISCAaft['Cumulative Return After Crash'].iloc[-1],DISCKaft['Cumulative Return After Crash'].iloc[-1],DISHaft['Cumulative Return After Crash'].iloc[-1],EAaft['Cumulative Return After Crash'].iloc[-1],FBaft['Cumulative Return After Crash'].iloc[-1],FOXAaft['Cumulative Return After Crash'].iloc[-1],FOXaft['Cumulative Return After Crash'].iloc[-1],IPGaft['Cumulative Return After Crash'].iloc[-1],LYVaft['Cumulative Return After Crash'].iloc[-1],NFLXaft['Cumulative Return After Crash'].iloc[-1],NWSAaft['Cumulative Return After Crash'].iloc[-1],NWSaft['Cumulative Return After Crash'].iloc[-1],OMCaft['Cumulative Return After Crash'].iloc[-1],TMUSaft['Cumulative Return After Crash'].iloc[-1],TTWOaft['Cumulative Return After Crash'].iloc[-1],TWTRaft['Cumulative Return After Crash'].iloc[-1],VZaft['Cumulative Return After Crash'].iloc[-1],VIACaft['Cumulative Return After Crash'].iloc[-1],DISaft['Cumulative Return After Crash'].iloc[-1]]}                         

csdf2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','ATVI','GOOGL','GOOG','T','CTL','CHTR','CMCSA','DISCA','DISCK','DISH','EA','FB','FOXA','FOX','IPG','LYV','NFLX','NWSA','NWS','OMC','TMUS','TTWO','TWTR','VZ','VIAC','DIS'])

pd.DataFrame(csdf2)
Out[613]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
ATVI 1.288604 1.187825 0.882344 1.229502
GOOGL 1.091801 1.150159 0.694883 1.366074
GOOG 1.089244 1.149643 0.695992 1.361313
T 0.860105 1.013386 0.693344 1.224131
CTL 0.758065 0.937588 0.649963 1.243959
CHTR 1.148620 1.167017 0.708023 1.390120
CMCSA 0.967364 1.057688 0.742503 1.231782
DISCA 0.769565 0.931056 0.616744 1.340184
DISCK 0.746742 0.955563 0.575524 1.357837
DISH 1.070212 1.224978 0.442683 1.973554
EA 1.175793 1.085163 0.874267 1.239342
FB 1.161341 1.079865 0.690186 1.558204
FOXA 0.869014 1.043099 0.545504 1.527228
FOX 0.873268 1.054850 0.542146 1.527007
IPG 0.874774 1.127034 0.516647 1.502329
LYV 0.819804 1.088946 0.493391 1.525850
NFLX 1.378812 1.268402 0.933342 1.164682
NWSA 1.009346 1.133178 0.565636 1.574727
NWS 0.997705 1.146136 0.543391 1.601966
OMC 0.772889 1.001780 0.613100 1.258385
TMUS 1.290911 1.270105 0.760804 1.335931
TTWO 1.056986 0.943998 0.953878 1.173830
TWTR 1.162333 1.301667 0.632266 1.412313
VZ 0.953907 0.959689 0.866070 1.147684
VIAC 0.590308 0.716838 0.385114 2.138298
DIS 0.841786 0.946655 0.610957 1.455457
In [614]:
csdf2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[614]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
VIAC 0.590308 0.716838 0.385114 2.138298
DISH 1.070212 1.224978 0.442683 1.973554
NWS 0.997705 1.146136 0.543391 1.601966
NWSA 1.009346 1.133178 0.565636 1.574727
FB 1.161341 1.079865 0.690186 1.558204
FOXA 0.869014 1.043099 0.545504 1.527228
FOX 0.873268 1.054850 0.542146 1.527007
LYV 0.819804 1.088946 0.493391 1.525850
IPG 0.874774 1.127034 0.516647 1.502329
DIS 0.841786 0.946655 0.610957 1.455457
S&P 500 1.026077 1.083678 0.663281 1.427519
TWTR 1.162333 1.301667 0.632266 1.412313
CHTR 1.148620 1.167017 0.708023 1.390120
GOOGL 1.091801 1.150159 0.694883 1.366074
GOOG 1.089244 1.149643 0.695992 1.361313
DISCK 0.746742 0.955563 0.575524 1.357837
DISCA 0.769565 0.931056 0.616744 1.340184
TMUS 1.290911 1.270105 0.760804 1.335931
OMC 0.772889 1.001780 0.613100 1.258385
CTL 0.758065 0.937588 0.649963 1.243959
EA 1.175793 1.085163 0.874267 1.239342
CMCSA 0.967364 1.057688 0.742503 1.231782
ATVI 1.288604 1.187825 0.882344 1.229502
T 0.860105 1.013386 0.693344 1.224131
TTWO 1.056986 0.943998 0.953878 1.173830
NFLX 1.378812 1.268402 0.933342 1.164682
VZ 0.953907 0.959689 0.866070 1.147684
In [615]:
#Table of Key Statistics of Communication Services Stocks
import pandas as pd
import statistics as stats

csmeans = [ATVImean,GOOGLmean,GOOGmean,Tmean,CTLmean,CHTRmean,CMCSAmean,DISCAmean,DISCKmean,DISHmean,EAmean,FBmean,FOXAmean,FOXmean,IPGmean,LYVmean,NFLXmean,NWSAmean,NWSmean,OMCmean,TMUSmean,TTWOmean,TWTRmean,VZmean,VIACmean,DISmean]
csaveragemean = stats.mean(csmeans)
csvariances = [ATVIvar,GOOGLvar,GOOGvar,Tvar,CTLvar,CHTRvar,CMCSAvar,DISCAvar,DISCKvar,DISHvar,EAvar,FBvar,FOXAvar,FOXvar,IPGvar,LYVvar,NFLXvar,NWSAvar,NWSvar,OMCvar,TMUSvar,TTWOvar,TWTRvar,VZvar,VIACvar,DISvar]
csaveragevar = stats.mean(csvariances)
cskurtoses = [ATVIkurt,GOOGLkurt,GOOGkurt,Tkurt,CTLkurt,CHTRkurt,CMCSAkurt,DISCAkurt,DISCKkurt,DISHkurt,EAkurt,FBkurt,FOXAkurt,FOXkurt,IPGkurt,LYVkurt,NFLXkurt,NWSAkurt,NWSkurt,OMCkurt,TMUSkurt,TTWOkurt,TWTRkurt,VZkurt,VIACkurt,DISkurt]
csaveragekurt = stats.mean(cskurtoses)
csskews = [ATVIskew,GOOGLskew,GOOGskew,Tskew,CTLskew,CHTRskew,CMCSAskew,DISCAskew,DISCKskew,DISHskew,EAskew,FBskew,FOXAskew,FOXskew,IPGskew,LYVskew,NFLXskew,NWSAskew,NWSskew,OMCskew,TMUSskew,TTWOskew,TWTRskew,VZskew,VIACskew,DISskew]
csaverageskew = stats.mean(csskews)

Communication_Services = {'Mean': [[spmean],[csaveragemean],[ATVImean], [GOOGLmean],[GOOGmean],[Tmean],[CTLmean],[CHTRmean],[CMCSAmean],[DISCAmean],[DISCKmean],[DISHmean],[EAmean],[FBmean],[FOXAmean],[FOXmean],[IPGmean],[LYVmean],[NFLXmean],[NWSAmean],[NWSmean],[OMCmean],[TMUSmean],[TTWOmean],[TWTRmean],[VZmean],[VIACmean],[DISmean]], 
        'Variance': [[spvar],[csaveragevar],[ATVIvar],[GOOGLvar],[GOOGvar],[Tvar],[CTLvar],[CHTRvar],[CMCSAvar],[DISCAvar],[DISCKvar],[DISHvar],[EAvar],[FBvar],[FOXAvar],[FOXvar],[IPGvar],[LYVvar],[NFLXvar],[NWSAvar],[NWSvar],[OMCvar],[TMUSvar],[TTWOvar],[TWTRvar],[VZvar],[VIACvar],[DISvar]], 
          'Kurtosis': [[spkurt],[csaveragekurt],[ATVIkurt],[GOOGLkurt],[GOOGkurt],[Tkurt],[CTLkurt],[CHTRkurt],[CMCSAkurt],[DISCAkurt],[DISCKkurt],[DISHkurt],[EAkurt],[FBkurt],[FOXAkurt],[FOXkurt],[IPGkurt],[LYVkurt],[NFLXkurt],[NWSAkurt],[NWSkurt],[OMCkurt],[TMUSkurt],[TTWOkurt],[TWTRkurt],[VZkurt],[VIACkurt],[DISkurt]],
          'Skew': [[spskew],[csaverageskew],[ATVIskew],[GOOGLskew],[GOOGskew],[Tskew],[CTLskew],[CHTRskew],[CMCSAskew],[DISCAskew],[DISCKskew],[DISHskew],[EAskew],[FBskew],[FOXAskew],[FOXskew],[IPGskew],[LYVskew],[NFLXskew],[NWSAskew],[NWSskew],[OMCskew],[TMUSskew],[TTWOskew],[TWTRskew],[VZskew],[VIACskew],[DISskew]],
        }

csdf = pd.DataFrame(Communication_Services, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','ATVI','GOOGL','GOOG','T','CTL','CHTR','CMCSA','DISCA','DISCK','DISH','EA','FB','FOXA','FOX','IPG','LYV','NFLX','NWSA','NWS','OMC','TMUS','TTWO','TWTR','VZ','VIAC','DIS']
)

pd.DataFrame(csdf)
Out[615]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [0.0003912645853900008] [0.0013372232036933404] [3.1238021784062884] [0.0038761459127950137]
ATVI [0.0024316557803352196] [0.0008432106214535389] [2.325927486287524] [0.10322939842751226]
GOOGL [0.0011041486296245637] [0.0008164604953023678] [3.622599465446168] [-0.17366946025239532]
GOOG [0.0010771409691408649] [0.0008005456021889158] [3.480478576296411] [-0.11873340362588365]
T [-0.0007906166744219113] [0.0008137990208812876] [2.449093445861239] [0.009127153941740965]
CTL [-0.0013904056651822322] [0.00161729978016048] [2.4974579450332843] [0.047260041421284704]
CHTR [0.0014566816627220565] [0.0007072199697124268] [8.195385338636989] [-0.7746286666682107]
CMCSA [0.00018184083254306637] [0.0009025625433914207] [3.050386299645714] [0.3983432679826314]
DISCA [-0.0014227112472993898] [0.001306305218575184] [1.1537863950102665] [-0.2268891808258826]
DISCK [-0.0016631916015768901] [0.0013006335687361743] [1.513729243554791] [-0.25798464627632045]
DISH [0.0017379439974189149] [0.00239652551584026] [2.4515436957249364] [-0.14919333631886397]
EA [0.0016384124787332973] [0.0007145828150997077] [3.8094271226488248] [0.3703120720171359]
FB [0.00168488099945279] [0.00099060210240469] [4.189864651115537] [-0.4821631415997356]
FOXA [-0.0004922434600235931] [0.0012513916441011894] [2.1587083413762738] [0.09597575176295897]
FOX [-0.000459789211528625] [0.0012375942770203006] [2.3433749835138027] [0.05599385307012235]
IPG [-0.00010934966290010362] [0.001909215384104062] [2.4295503839131145] [-0.015476969105207062]
LYV [7.969565582033498e-05] [0.0033062437782566687] [1.596742312656274] [-0.07480135515521928]
NFLX [0.003000661223580784] [0.0008997461729473019] [2.0792421569787196] [-0.24534688909415422]
NWSA [0.0007719544072668082] [0.0014182995179859912] [2.0753300914303576] [0.4011034576350257]
NWS [0.0007185922083344496] [0.0014822318226790955] [2.7721177954795317] [0.03558945854758807]
OMC [-0.0014452651148577967] [0.0012006802228962208] [1.647355046281013] [0.04590623956390266]
TMUS [0.002544777123106954] [0.0010432351662422522] [4.049203575910615] [0.15633791779205242]
TTWO [0.0009208276719758804] [0.0009589149289676753] [2.937066399258142] [-0.4592801915007289]
TWTR [0.002130582203372295] [0.0018621454778007898] [3.8143040734558067] [-0.33534975028594044]
VZ [-0.00017706776608117546] [0.0004008381048884117] [3.2722608120363548] [0.6523079234225209]
VIAC [-0.0026158945604787417] [0.003325390985281676] [7.44412182316165] [0.8155810956049909]
DIS [-0.0007403816589377992] [0.0012621285591087638] [3.859799177850153] [0.22722915325174542]
In [616]:
csdf.sort_values("Mean", ascending=False)
Out[616]:
Mean Variance Kurtosis Skew
NFLX [0.003000661223580784] [0.0008997461729473019] [2.0792421569787196] [-0.24534688909415422]
TMUS [0.002544777123106954] [0.0010432351662422522] [4.049203575910615] [0.15633791779205242]
ATVI [0.0024316557803352196] [0.0008432106214535389] [2.325927486287524] [0.10322939842751226]
TWTR [0.002130582203372295] [0.0018621454778007898] [3.8143040734558067] [-0.33534975028594044]
DISH [0.0017379439974189149] [0.00239652551584026] [2.4515436957249364] [-0.14919333631886397]
FB [0.00168488099945279] [0.00099060210240469] [4.189864651115537] [-0.4821631415997356]
EA [0.0016384124787332973] [0.0007145828150997077] [3.8094271226488248] [0.3703120720171359]
CHTR [0.0014566816627220565] [0.0007072199697124268] [8.195385338636989] [-0.7746286666682107]
GOOGL [0.0011041486296245637] [0.0008164604953023678] [3.622599465446168] [-0.17366946025239532]
GOOG [0.0010771409691408649] [0.0008005456021889158] [3.480478576296411] [-0.11873340362588365]
TTWO [0.0009208276719758804] [0.0009589149289676753] [2.937066399258142] [-0.4592801915007289]
NWSA [0.0007719544072668082] [0.0014182995179859912] [2.0753300914303576] [0.4011034576350257]
NWS [0.0007185922083344496] [0.0014822318226790955] [2.7721177954795317] [0.03558945854758807]
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [0.0003912645853900008] [0.0013372232036933404] [3.1238021784062884] [0.0038761459127950137]
CMCSA [0.00018184083254306637] [0.0009025625433914207] [3.050386299645714] [0.3983432679826314]
LYV [7.969565582033498e-05] [0.0033062437782566687] [1.596742312656274] [-0.07480135515521928]
IPG [-0.00010934966290010362] [0.001909215384104062] [2.4295503839131145] [-0.015476969105207062]
VZ [-0.00017706776608117546] [0.0004008381048884117] [3.2722608120363548] [0.6523079234225209]
FOX [-0.000459789211528625] [0.0012375942770203006] [2.3433749835138027] [0.05599385307012235]
FOXA [-0.0004922434600235931] [0.0012513916441011894] [2.1587083413762738] [0.09597575176295897]
DIS [-0.0007403816589377992] [0.0012621285591087638] [3.859799177850153] [0.22722915325174542]
T [-0.0007906166744219113] [0.0008137990208812876] [2.449093445861239] [0.009127153941740965]
CTL [-0.0013904056651822322] [0.00161729978016048] [2.4974579450332843] [0.047260041421284704]
DISCA [-0.0014227112472993898] [0.001306305218575184] [1.1537863950102665] [-0.2268891808258826]
OMC [-0.0014452651148577967] [0.0012006802228962208] [1.647355046281013] [0.04590623956390266]
DISCK [-0.0016631916015768901] [0.0013006335687361743] [1.513729243554791] [-0.25798464627632045]
VIAC [-0.0026158945604787417] [0.003325390985281676] [7.44412182316165] [0.8155810956049909]

Utilities

In [617]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AES = web.DataReader('AES', 'yahoo', start, end)
AES['returns'] = AES['Close'].pct_change(1)
AES['Cumulative Return'] = (1 + AES['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AESmean = AES['returns'].mean()
AESvar = AES['returns'].var()
AESkurt = AES['returns'].kurt()
AESskew = AES['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AESbef = web.DataReader('AES', 'yahoo', start, crashstart)
AESbef['avgreturnbefore'] = AESbef['Close'].pct_change(1)
AESbef['Cumulative Return Before Crash'] = (1 + AESbef['avgreturnbefore']).cumprod()

#period of crash
AESdur = web.DataReader('AES', 'yahoo', crashstart, crashend)
AESdur['avgreturnduring'] = AESdur['Close'].pct_change(1)
AESdur['Cumulative Return During Crash'] = (1 + AESdur['avgreturnduring']).cumprod()

#period after crash
AESaft = web.DataReader('AES', 'yahoo', crashend, end)
AESaft['avgreturnafter'] = AESaft['Close'].pct_change(1)
AESaft['Cumulative Return After Crash'] = (1 + AESaft['avgreturnafter']).cumprod()
In [618]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
LNT = web.DataReader('LNT', 'yahoo', start, end)
LNT['returns'] = LNT['Close'].pct_change(1)
LNT['Cumulative Return'] = (1 + LNT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
LNTmean = LNT['returns'].mean()
LNTvar = LNT['returns'].var()
LNTkurt = LNT['returns'].kurt()
LNTskew = LNT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
LNTbef = web.DataReader('LNT', 'yahoo', start, crashstart)
LNTbef['avgreturnbefore'] = LNTbef['Close'].pct_change(1)
LNTbef['Cumulative Return Before Crash'] = (1 + LNTbef['avgreturnbefore']).cumprod()

#period of crash
LNTdur = web.DataReader('LNT', 'yahoo', crashstart, crashend)
LNTdur['avgreturnduring'] = LNTdur['Close'].pct_change(1)
LNTdur['Cumulative Return During Crash'] = (1 + LNTdur['avgreturnduring']).cumprod()

#period after crash
LNTaft = web.DataReader('LNT', 'yahoo', crashend, end)
LNTaft['avgreturnafter'] = LNTaft['Close'].pct_change(1)
LNTaft['Cumulative Return After Crash'] = (1 + LNTaft['avgreturnafter']).cumprod()
In [619]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AEE = web.DataReader('AEE', 'yahoo', start, end)
AEE['returns'] = AEE['Close'].pct_change(1)
AEE['Cumulative Return'] = (1 + AEE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AEEmean = AEE['returns'].mean()
AEEvar = AEE['returns'].var()
AEEkurt = AEE['returns'].kurt()
AEEskew = AEE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AEEbef = web.DataReader('AEE', 'yahoo', start, crashstart)
AEEbef['avgreturnbefore'] = AEEbef['Close'].pct_change(1)
AEEbef['Cumulative Return Before Crash'] = (1 + AEEbef['avgreturnbefore']).cumprod()

#period of crash
AEEdur = web.DataReader('AEE', 'yahoo', crashstart, crashend)
AEEdur['avgreturnduring'] = AEEdur['Close'].pct_change(1)
AEEdur['Cumulative Return During Crash'] = (1 + AEEdur['avgreturnduring']).cumprod()

#period after crash
AEEaft = web.DataReader('AEE', 'yahoo', crashend, end)
AEEaft['avgreturnafter'] = AEEaft['Close'].pct_change(1)
AEEaft['Cumulative Return After Crash'] = (1 + AEEaft['avgreturnafter']).cumprod()
In [620]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AEP = web.DataReader('AEP', 'yahoo', start, end)
AEP['returns'] = AEP['Close'].pct_change(1)
AEP['Cumulative Return'] = (1 + AEP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AEPmean = AEP['returns'].mean()
AEPvar = AEP['returns'].var()
AEPkurt = AEP['returns'].kurt()
AEPskew = AEP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AEPbef = web.DataReader('AEP', 'yahoo', start, crashstart)
AEPbef['avgreturnbefore'] = AEPbef['Close'].pct_change(1)
AEPbef['Cumulative Return Before Crash'] = (1 + AEPbef['avgreturnbefore']).cumprod()

#period of crash
AEPdur = web.DataReader('AEP', 'yahoo', crashstart, crashend)
AEPdur['avgreturnduring'] = AEPdur['Close'].pct_change(1)
AEPdur['Cumulative Return During Crash'] = (1 + AEPdur['avgreturnduring']).cumprod()

#period after crash
AEPaft = web.DataReader('AEP', 'yahoo', crashend, end)
AEPaft['avgreturnafter'] = AEPaft['Close'].pct_change(1)
AEPaft['Cumulative Return After Crash'] = (1 + AEPaft['avgreturnafter']).cumprod()
In [621]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AWK = web.DataReader('AWK', 'yahoo', start, end)
AWK['returns'] = AWK['Close'].pct_change(1)
AWK['Cumulative Return'] = (1 + AWK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AWKmean = AWK['returns'].mean()
AWKvar = AWK['returns'].var()
AWKkurt = AWK['returns'].kurt()
AWKskew = AWK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AWKbef = web.DataReader('AWK', 'yahoo', start, crashstart)
AWKbef['avgreturnbefore'] = AWKbef['Close'].pct_change(1)
AWKbef['Cumulative Return Before Crash'] = (1 + AWKbef['avgreturnbefore']).cumprod()

#period of crash
AWKdur = web.DataReader('AWK', 'yahoo', crashstart, crashend)
AWKdur['avgreturnduring'] = AWKdur['Close'].pct_change(1)
AWKdur['Cumulative Return During Crash'] = (1 + AWKdur['avgreturnduring']).cumprod()

#period after crash
AWKaft = web.DataReader('AWK', 'yahoo', crashend, end)
AWKaft['avgreturnafter'] = AWKaft['Close'].pct_change(1)
AWKaft['Cumulative Return After Crash'] = (1 + AWKaft['avgreturnafter']).cumprod()
In [622]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ATO = web.DataReader('ATO', 'yahoo', start, end)
ATO['returns'] = ATO['Close'].pct_change(1)
ATO['Cumulative Return'] = (1 + ATO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ATOmean = ATO['returns'].mean()
ATOvar = ATO['returns'].var()
ATOkurt = ATO['returns'].kurt()
ATOskew = ATO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ATObef = web.DataReader('ATO', 'yahoo', start, crashstart)
ATObef['avgreturnbefore'] = ATObef['Close'].pct_change(1)
ATObef['Cumulative Return Before Crash'] = (1 + ATObef['avgreturnbefore']).cumprod()

#period of crash
ATOdur = web.DataReader('ATO', 'yahoo', crashstart, crashend)
ATOdur['avgreturnduring'] = ATOdur['Close'].pct_change(1)
ATOdur['Cumulative Return During Crash'] = (1 + ATOdur['avgreturnduring']).cumprod()

#period after crash
ATOaft = web.DataReader('ATO', 'yahoo', crashend, end)
ATOaft['avgreturnafter'] = ATOaft['Close'].pct_change(1)
ATOaft['Cumulative Return After Crash'] = (1 + ATOaft['avgreturnafter']).cumprod()
In [623]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CNP = web.DataReader('CNP', 'yahoo', start, end)
CNP['returns'] = CNP['Close'].pct_change(1)
CNP['Cumulative Return'] = (1 + CNP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CNPmean = CNP['returns'].mean()
CNPvar = CNP['returns'].var()
CNPkurt = CNP['returns'].kurt()
CNPskew = CNP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CNPbef = web.DataReader('CNP', 'yahoo', start, crashstart)
CNPbef['avgreturnbefore'] = CNPbef['Close'].pct_change(1)
CNPbef['Cumulative Return Before Crash'] = (1 + CNPbef['avgreturnbefore']).cumprod()

#period of crash
CNPdur = web.DataReader('CNP', 'yahoo', crashstart, crashend)
CNPdur['avgreturnduring'] = CNPdur['Close'].pct_change(1)
CNPdur['Cumulative Return During Crash'] = (1 + CNPdur['avgreturnduring']).cumprod()

#period after crash
CNPaft = web.DataReader('CNP', 'yahoo', crashend, end)
CNPaft['avgreturnafter'] = CNPaft['Close'].pct_change(1)
CNPaft['Cumulative Return After Crash'] = (1 + CNPaft['avgreturnafter']).cumprod()
In [624]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CMS = web.DataReader('CMS', 'yahoo', start, end)
CMS['returns'] = CMS['Close'].pct_change(1)
CMS['Cumulative Return'] = (1 + CMS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CMSmean = CMS['returns'].mean()
CMSvar = CMS['returns'].var()
CMSkurt = CMS['returns'].kurt()
CMSskew = CMS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CMSbef = web.DataReader('CMS', 'yahoo', start, crashstart)
CMSbef['avgreturnbefore'] = CMSbef['Close'].pct_change(1)
CMSbef['Cumulative Return Before Crash'] = (1 + CMSbef['avgreturnbefore']).cumprod()

#period of crash
CMSdur = web.DataReader('CMS', 'yahoo', crashstart, crashend)
CMSdur['avgreturnduring'] = CMSdur['Close'].pct_change(1)
CMSdur['Cumulative Return During Crash'] = (1 + CMSdur['avgreturnduring']).cumprod()

#period after crash
CMSaft = web.DataReader('CMS', 'yahoo', crashend, end)
CMSaft['avgreturnafter'] = CMSaft['Close'].pct_change(1)
CMSaft['Cumulative Return After Crash'] = (1 + CMSaft['avgreturnafter']).cumprod()
In [625]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ED = web.DataReader('ED', 'yahoo', start, end)
ED['returns'] = ED['Close'].pct_change(1)
ED['Cumulative Return'] = (1 + ED['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EDmean = ED['returns'].mean()
EDvar = ED['returns'].var()
EDkurt = ED['returns'].kurt()
EDskew = ED['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EDbef = web.DataReader('ED', 'yahoo', start, crashstart)
EDbef['avgreturnbefore'] = EDbef['Close'].pct_change(1)
EDbef['Cumulative Return Before Crash'] = (1 + EDbef['avgreturnbefore']).cumprod()

#period of crash
EDdur = web.DataReader('ED', 'yahoo', crashstart, crashend)
EDdur['avgreturnduring'] = EDdur['Close'].pct_change(1)
EDdur['Cumulative Return During Crash'] = (1 + EDdur['avgreturnduring']).cumprod()

#period after crash
EDaft = web.DataReader('ED', 'yahoo', crashend, end)
EDaft['avgreturnafter'] = EDaft['Close'].pct_change(1)
EDaft['Cumulative Return After Crash'] = (1 + EDaft['avgreturnafter']).cumprod()
In [626]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
D = web.DataReader('D', 'yahoo', start, end)
D['returns'] = D['Close'].pct_change(1)
D['Cumulative Return'] = (1 + D['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
Dmean = D['returns'].mean()
Dvar = D['returns'].var()
Dkurt = D['returns'].kurt()
Dskew = D['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
Dbef = web.DataReader('D', 'yahoo', start, crashstart)
Dbef['avgreturnbefore'] = Dbef['Close'].pct_change(1)
Dbef['Cumulative Return Before Crash'] = (1 + Dbef['avgreturnbefore']).cumprod()

#period of crash
Ddur = web.DataReader('D', 'yahoo', crashstart, crashend)
Ddur['avgreturnduring'] = Ddur['Close'].pct_change(1)
Ddur['Cumulative Return During Crash'] = (1 + Ddur['avgreturnduring']).cumprod()

#period after crash
Daft = web.DataReader('D', 'yahoo', crashend, end)
Daft['avgreturnafter'] = Daft['Close'].pct_change(1)
Daft['Cumulative Return After Crash'] = (1 + Daft['avgreturnafter']).cumprod()
In [627]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DTE = web.DataReader('DTE', 'yahoo', start, end)
DTE['returns'] = DTE['Close'].pct_change(1)
DTE['Cumulative Return'] = (1 + DTE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DTEmean = DTE['returns'].mean()
DTEvar = DTE['returns'].var()
DTEkurt = DTE['returns'].kurt()
DTEskew = DTE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DTEbef = web.DataReader('DTE', 'yahoo', start, crashstart)
DTEbef['avgreturnbefore'] = DTEbef['Close'].pct_change(1)
DTEbef['Cumulative Return Before Crash'] = (1 + DTEbef['avgreturnbefore']).cumprod()

#period of crash
DTEdur = web.DataReader('DTE', 'yahoo', crashstart, crashend)
DTEdur['avgreturnduring'] = DTEdur['Close'].pct_change(1)
DTEdur['Cumulative Return During Crash'] = (1 + DTEdur['avgreturnduring']).cumprod()

#period after crash
DTEaft = web.DataReader('DTE', 'yahoo', crashend, end)
DTEaft['avgreturnafter'] = DTEaft['Close'].pct_change(1)
DTEaft['Cumulative Return After Crash'] = (1 + DTEaft['avgreturnafter']).cumprod()
In [628]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DUK = web.DataReader('DUK', 'yahoo', start, end)
DUK['returns'] = DUK['Close'].pct_change(1)
DUK['Cumulative Return'] = (1 + DUK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DUKmean = DUK['returns'].mean()
DUKvar = DUK['returns'].var()
DUKkurt = DUK['returns'].kurt()
DUKskew = DUK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DUKbef = web.DataReader('DUK', 'yahoo', start, crashstart)
DUKbef['avgreturnbefore'] = DUKbef['Close'].pct_change(1)
DUKbef['Cumulative Return Before Crash'] = (1 + DUKbef['avgreturnbefore']).cumprod()

#period of crash
DUKdur = web.DataReader('DUK', 'yahoo', crashstart, crashend)
DUKdur['avgreturnduring'] = DUKdur['Close'].pct_change(1)
DUKdur['Cumulative Return During Crash'] = (1 + DUKdur['avgreturnduring']).cumprod()

#period after crash
DUKaft = web.DataReader('DUK', 'yahoo', crashend, end)
DUKaft['avgreturnafter'] = DUKaft['Close'].pct_change(1)
DUKaft['Cumulative Return After Crash'] = (1 + DUKaft['avgreturnafter']).cumprod()
In [629]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EIX = web.DataReader('EIX', 'yahoo', start, end)
EIX['returns'] = EIX['Close'].pct_change(1)
EIX['Cumulative Return'] = (1 + EIX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EIXmean = EIX['returns'].mean()
EIXvar = EIX['returns'].var()
EIXkurt = EIX['returns'].kurt()
EIXskew = EIX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EIXbef = web.DataReader('EIX', 'yahoo', start, crashstart)
EIXbef['avgreturnbefore'] = EIXbef['Close'].pct_change(1)
EIXbef['Cumulative Return Before Crash'] = (1 + EIXbef['avgreturnbefore']).cumprod()

#period of crash
EIXdur = web.DataReader('EIX', 'yahoo', crashstart, crashend)
EIXdur['avgreturnduring'] = EIXdur['Close'].pct_change(1)
EIXdur['Cumulative Return During Crash'] = (1 + EIXdur['avgreturnduring']).cumprod()

#period after crash
EIXaft = web.DataReader('EIX', 'yahoo', crashend, end)
EIXaft['avgreturnafter'] = EIXaft['Close'].pct_change(1)
EIXaft['Cumulative Return After Crash'] = (1 + EIXaft['avgreturnafter']).cumprod()
In [630]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ETR = web.DataReader('ETR', 'yahoo', start, end)
ETR['returns'] = ETR['Close'].pct_change(1)
ETR['Cumulative Return'] = (1 + ETR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ETRmean = ETR['returns'].mean()
ETRvar = ETR['returns'].var()
ETRkurt = ETR['returns'].kurt()
ETRskew = ETR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ETRbef = web.DataReader('ETR', 'yahoo', start, crashstart)
ETRbef['avgreturnbefore'] = ETRbef['Close'].pct_change(1)
ETRbef['Cumulative Return Before Crash'] = (1 + ETRbef['avgreturnbefore']).cumprod()

#period of crash
ETRdur = web.DataReader('ETR', 'yahoo', crashstart, crashend)
ETRdur['avgreturnduring'] = ETRdur['Close'].pct_change(1)
ETRdur['Cumulative Return During Crash'] = (1 + ETRdur['avgreturnduring']).cumprod()

#period after crash
ETRaft = web.DataReader('ETR', 'yahoo', crashend, end)
ETRaft['avgreturnafter'] = ETRaft['Close'].pct_change(1)
ETRaft['Cumulative Return After Crash'] = (1 + ETRaft['avgreturnafter']).cumprod()
In [631]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EVRG = web.DataReader('EVRG', 'yahoo', start, end)
EVRG['returns'] = EVRG['Close'].pct_change(1)
EVRG['Cumulative Return'] = (1 + EVRG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EVRGmean = EVRG['returns'].mean()
EVRGvar = EVRG['returns'].var()
EVRGkurt = EVRG['returns'].kurt()
EVRGskew = EVRG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EVRGbef = web.DataReader('EVRG', 'yahoo', start, crashstart)
EVRGbef['avgreturnbefore'] = EVRGbef['Close'].pct_change(1)
EVRGbef['Cumulative Return Before Crash'] = (1 + EVRGbef['avgreturnbefore']).cumprod()

#period of crash
EVRGdur = web.DataReader('EVRG', 'yahoo', crashstart, crashend)
EVRGdur['avgreturnduring'] = EVRGdur['Close'].pct_change(1)
EVRGdur['Cumulative Return During Crash'] = (1 + EVRGdur['avgreturnduring']).cumprod()

#period after crash
EVRGaft = web.DataReader('EVRG', 'yahoo', crashend, end)
EVRGaft['avgreturnafter'] = EVRGaft['Close'].pct_change(1)
EVRGaft['Cumulative Return After Crash'] = (1 + EVRGaft['avgreturnafter']).cumprod()
In [632]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ES = web.DataReader('ES', 'yahoo', start, end)
ES['returns'] = ES['Close'].pct_change(1)
ES['Cumulative Return'] = (1 + ES['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ESmean = ES['returns'].mean()
ESvar = ES['returns'].var()
ESkurt = ES['returns'].kurt()
ESskew = ES['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ESbef = web.DataReader('ES', 'yahoo', start, crashstart)
ESbef['avgreturnbefore'] = ESbef['Close'].pct_change(1)
ESbef['Cumulative Return Before Crash'] = (1 + ESbef['avgreturnbefore']).cumprod()

#period of crash
ESdur = web.DataReader('ES', 'yahoo', crashstart, crashend)
ESdur['avgreturnduring'] = ESdur['Close'].pct_change(1)
ESdur['Cumulative Return During Crash'] = (1 + ESdur['avgreturnduring']).cumprod()

#period after crash
ESaft = web.DataReader('ES', 'yahoo', crashend, end)
ESaft['avgreturnafter'] = ESaft['Close'].pct_change(1)
ESaft['Cumulative Return After Crash'] = (1 + ESaft['avgreturnafter']).cumprod()
In [633]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EXC = web.DataReader('EXC', 'yahoo', start, end)
EXC['returns'] = EXC['Close'].pct_change(1)
EXC['Cumulative Return'] = (1 + EXC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EXCmean = EXC['returns'].mean()
EXCvar = EXC['returns'].var()
EXCkurt = EXC['returns'].kurt()
EXCskew = EXC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EXCbef = web.DataReader('EXC', 'yahoo', start, crashstart)
EXCbef['avgreturnbefore'] = EXCbef['Close'].pct_change(1)
EXCbef['Cumulative Return Before Crash'] = (1 + EXCbef['avgreturnbefore']).cumprod()

#period of crash
EXCdur = web.DataReader('EXC', 'yahoo', crashstart, crashend)
EXCdur['avgreturnduring'] = EXCdur['Close'].pct_change(1)
EXCdur['Cumulative Return During Crash'] = (1 + EXCdur['avgreturnduring']).cumprod()

#period after crash
EXCaft = web.DataReader('EXC', 'yahoo', crashend, end)
EXCaft['avgreturnafter'] = EXCaft['Close'].pct_change(1)
EXCaft['Cumulative Return After Crash'] = (1 + EXCaft['avgreturnafter']).cumprod()
In [634]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FE = web.DataReader('FE', 'yahoo', start, end)
FE['returns'] = FE['Close'].pct_change(1)
FE['Cumulative Return'] = (1 + FE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FEmean = FE['returns'].mean()
FEvar = FE['returns'].var()
FEkurt = FE['returns'].kurt()
FEskew = FE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FEbef = web.DataReader('FE', 'yahoo', start, crashstart)
FEbef['avgreturnbefore'] = FEbef['Close'].pct_change(1)
FEbef['Cumulative Return Before Crash'] = (1 + FEbef['avgreturnbefore']).cumprod()

#period of crash
FEdur = web.DataReader('FE', 'yahoo', crashstart, crashend)
FEdur['avgreturnduring'] = FEdur['Close'].pct_change(1)
FEdur['Cumulative Return During Crash'] = (1 + FEdur['avgreturnduring']).cumprod()

#period after crash
FEaft = web.DataReader('FE', 'yahoo', crashend, end)
FEaft['avgreturnafter'] = FEaft['Close'].pct_change(1)
FEaft['Cumulative Return After Crash'] = (1 + FEaft['avgreturnafter']).cumprod()
In [635]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NEE = web.DataReader('NEE', 'yahoo', start, end)
NEE['returns'] = NEE['Close'].pct_change(1)
NEE['Cumulative Return'] = (1 + NEE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NEEmean = NEE['returns'].mean()
NEEvar = NEE['returns'].var()
NEEkurt = NEE['returns'].kurt()
NEEskew = NEE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NEEbef = web.DataReader('NEE', 'yahoo', start, crashstart)
NEEbef['avgreturnbefore'] = NEEbef['Close'].pct_change(1)
NEEbef['Cumulative Return Before Crash'] = (1 + NEEbef['avgreturnbefore']).cumprod()

#period of crash
NEEdur = web.DataReader('NEE', 'yahoo', crashstart, crashend)
NEEdur['avgreturnduring'] = NEEdur['Close'].pct_change(1)
NEEdur['Cumulative Return During Crash'] = (1 + NEEdur['avgreturnduring']).cumprod()

#period after crash
NEEaft = web.DataReader('NEE', 'yahoo', crashend, end)
NEEaft['avgreturnafter'] = NEEaft['Close'].pct_change(1)
NEEaft['Cumulative Return After Crash'] = (1 + NEEaft['avgreturnafter']).cumprod()
In [636]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NI = web.DataReader('NI', 'yahoo', start, end)
NI['returns'] = NI['Close'].pct_change(1)
NI['Cumulative Return'] = (1 + NI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NImean = NI['returns'].mean()
NIvar = NI['returns'].var()
NIkurt = NI['returns'].kurt()
NIskew = NI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NIbef = web.DataReader('NI', 'yahoo', start, crashstart)
NIbef['avgreturnbefore'] = NIbef['Close'].pct_change(1)
NIbef['Cumulative Return Before Crash'] = (1 + NIbef['avgreturnbefore']).cumprod()

#period of crash
NIdur = web.DataReader('NI', 'yahoo', crashstart, crashend)
NIdur['avgreturnduring'] = NIdur['Close'].pct_change(1)
NIdur['Cumulative Return During Crash'] = (1 + NIdur['avgreturnduring']).cumprod()

#period after crash
NIaft = web.DataReader('NI', 'yahoo', crashend, end)
NIaft['avgreturnafter'] = NIaft['Close'].pct_change(1)
NIaft['Cumulative Return After Crash'] = (1 + NIaft['avgreturnafter']).cumprod()
In [637]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
NRG = web.DataReader('NRG', 'yahoo', start, end)
NRG['returns'] = NRG['Close'].pct_change(1)
NRG['Cumulative Return'] = (1 + NRG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
NRGmean = NRG['returns'].mean()
NRGvar = NRG['returns'].var()
NRGkurt = NRG['returns'].kurt()
NRGskew = NRG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
NRGbef = web.DataReader('NRG', 'yahoo', start, crashstart)
NRGbef['avgreturnbefore'] = NRGbef['Close'].pct_change(1)
NRGbef['Cumulative Return Before Crash'] = (1 + NRGbef['avgreturnbefore']).cumprod()

#period of crash
NRGdur = web.DataReader('NRG', 'yahoo', crashstart, crashend)
NRGdur['avgreturnduring'] = NRGdur['Close'].pct_change(1)
NRGdur['Cumulative Return During Crash'] = (1 + NRGdur['avgreturnduring']).cumprod()

#period after crash
NRGaft = web.DataReader('NRG', 'yahoo', crashend, end)
NRGaft['avgreturnafter'] = NRGaft['Close'].pct_change(1)
NRGaft['Cumulative Return After Crash'] = (1 + NRGaft['avgreturnafter']).cumprod()
In [638]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PNW = web.DataReader('PNW', 'yahoo', start, end)
PNW['returns'] = PNW['Close'].pct_change(1)
PNW['Cumulative Return'] = (1 + PNW['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PNWmean = PNW['returns'].mean()
PNWvar = PNW['returns'].var()
PNWkurt = PNW['returns'].kurt()
PNWskew = PNW['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PNWbef = web.DataReader('PNW', 'yahoo', start, crashstart)
PNWbef['avgreturnbefore'] = PNWbef['Close'].pct_change(1)
PNWbef['Cumulative Return Before Crash'] = (1 + PNWbef['avgreturnbefore']).cumprod()

#period of crash
PNWdur = web.DataReader('PNW', 'yahoo', crashstart, crashend)
PNWdur['avgreturnduring'] = PNWdur['Close'].pct_change(1)
PNWdur['Cumulative Return During Crash'] = (1 + PNWdur['avgreturnduring']).cumprod()

#period after crash
PNWaft = web.DataReader('PNW', 'yahoo', crashend, end)
PNWaft['avgreturnafter'] = PNWaft['Close'].pct_change(1)
PNWaft['Cumulative Return After Crash'] = (1 + PNWaft['avgreturnafter']).cumprod()
In [639]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PPL = web.DataReader('PPL', 'yahoo', start, end)
PPL['returns'] = PPL['Close'].pct_change(1)
PPL['Cumulative Return'] = (1 + PPL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PPLmean = PPL['returns'].mean()
PPLvar = PPL['returns'].var()
PPLkurt = PPL['returns'].kurt()
PPLskew = PPL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PPLbef = web.DataReader('PPL', 'yahoo', start, crashstart)
PPLbef['avgreturnbefore'] = PPLbef['Close'].pct_change(1)
PPLbef['Cumulative Return Before Crash'] = (1 + PPLbef['avgreturnbefore']).cumprod()

#period of crash
PPLdur = web.DataReader('PPL', 'yahoo', crashstart, crashend)
PPLdur['avgreturnduring'] = PPLdur['Close'].pct_change(1)
PPLdur['Cumulative Return During Crash'] = (1 + PPLdur['avgreturnduring']).cumprod()

#period after crash
PPLaft = web.DataReader('PPL', 'yahoo', crashend, end)
PPLaft['avgreturnafter'] = PPLaft['Close'].pct_change(1)
PPLaft['Cumulative Return After Crash'] = (1 + PPLaft['avgreturnafter']).cumprod()
In [640]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PEG = web.DataReader('PEG', 'yahoo', start, end)
PEG['returns'] = PEG['Close'].pct_change(1)
PEG['Cumulative Return'] = (1 + PEG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PEGmean = PEG['returns'].mean()
PEGvar = PEG['returns'].var()
PEGkurt = PEG['returns'].kurt()
PEGskew = PEG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PEGbef = web.DataReader('PEG', 'yahoo', start, crashstart)
PEGbef['avgreturnbefore'] = PEGbef['Close'].pct_change(1)
PEGbef['Cumulative Return Before Crash'] = (1 + PEGbef['avgreturnbefore']).cumprod()

#period of crash
PEGdur = web.DataReader('PEG', 'yahoo', crashstart, crashend)
PEGdur['avgreturnduring'] = PEGdur['Close'].pct_change(1)
PEGdur['Cumulative Return During Crash'] = (1 + PEGdur['avgreturnduring']).cumprod()

#period after crash
PEGaft = web.DataReader('PEG', 'yahoo', crashend, end)
PEGaft['avgreturnafter'] = PEGaft['Close'].pct_change(1)
PEGaft['Cumulative Return After Crash'] = (1 + PEGaft['avgreturnafter']).cumprod()
In [641]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SRE = web.DataReader('SRE', 'yahoo', start, end)
SRE['returns'] = SRE['Close'].pct_change(1)
SRE['Cumulative Return'] = (1 + SRE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SREmean = SRE['returns'].mean()
SREvar = SRE['returns'].var()
SREkurt = SRE['returns'].kurt()
SREskew = SRE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SREbef = web.DataReader('SRE', 'yahoo', start, crashstart)
SREbef['avgreturnbefore'] = SREbef['Close'].pct_change(1)
SREbef['Cumulative Return Before Crash'] = (1 + SREbef['avgreturnbefore']).cumprod()

#period of crash
SREdur = web.DataReader('SRE', 'yahoo', crashstart, crashend)
SREdur['avgreturnduring'] = SREdur['Close'].pct_change(1)
SREdur['Cumulative Return During Crash'] = (1 + SREdur['avgreturnduring']).cumprod()

#period after crash
SREaft = web.DataReader('SRE', 'yahoo', crashend, end)
SREaft['avgreturnafter'] = SREaft['Close'].pct_change(1)
SREaft['Cumulative Return After Crash'] = (1 + SREaft['avgreturnafter']).cumprod()
In [642]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SO = web.DataReader('SO', 'yahoo', start, end)
SO['returns'] = SO['Close'].pct_change(1)
SO['Cumulative Return'] = (1 + SO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SOmean = SO['returns'].mean()
SOvar = SO['returns'].var()
SOkurt = SO['returns'].kurt()
SOskew = SO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SObef = web.DataReader('SO', 'yahoo', start, crashstart)
SObef['avgreturnbefore'] = SObef['Close'].pct_change(1)
SObef['Cumulative Return Before Crash'] = (1 + SObef['avgreturnbefore']).cumprod()

#period of crash
SOdur = web.DataReader('SO', 'yahoo', crashstart, crashend)
SOdur['avgreturnduring'] = SOdur['Close'].pct_change(1)
SOdur['Cumulative Return During Crash'] = (1 + SOdur['avgreturnduring']).cumprod()

#period after crash
SOaft = web.DataReader('SO', 'yahoo', crashend, end)
SOaft['avgreturnafter'] = SOaft['Close'].pct_change(1)
SOaft['Cumulative Return After Crash'] = (1 + SOaft['avgreturnafter']).cumprod()
In [643]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WEC = web.DataReader('WEC', 'yahoo', start, end)
WEC['returns'] = WEC['Close'].pct_change(1)
WEC['Cumulative Return'] = (1 + WEC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WECmean = WEC['returns'].mean()
WECvar = WEC['returns'].var()
WECkurt = WEC['returns'].kurt()
WECskew = WEC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WECbef = web.DataReader('WEC', 'yahoo', start, crashstart)
WECbef['avgreturnbefore'] = WECbef['Close'].pct_change(1)
WECbef['Cumulative Return Before Crash'] = (1 + WECbef['avgreturnbefore']).cumprod()

#period of crash
WECdur = web.DataReader('WEC', 'yahoo', crashstart, crashend)
WECdur['avgreturnduring'] = WECdur['Close'].pct_change(1)
WECdur['Cumulative Return During Crash'] = (1 + WECdur['avgreturnduring']).cumprod()

#period after crash
WECaft = web.DataReader('WEC', 'yahoo', crashend, end)
WECaft['avgreturnafter'] = WECaft['Close'].pct_change(1)
WECaft['Cumulative Return After Crash'] = (1 + WECaft['avgreturnafter']).cumprod()
In [644]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
XEL = web.DataReader('XEL', 'yahoo', start, end)
XEL['returns'] = XEL['Close'].pct_change(1)
XEL['Cumulative Return'] = (1 + XEL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
XELmean = XEL['returns'].mean()
XELvar = XEL['returns'].var()
XELkurt = XEL['returns'].kurt()
XELskew = XEL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
XELbef = web.DataReader('XEL', 'yahoo', start, crashstart)
XELbef['avgreturnbefore'] = XELbef['Close'].pct_change(1)
XELbef['Cumulative Return Before Crash'] = (1 + XELbef['avgreturnbefore']).cumprod()

#period of crash
XELdur = web.DataReader('XEL', 'yahoo', crashstart, crashend)
XELdur['avgreturnduring'] = XELdur['Close'].pct_change(1)
XELdur['Cumulative Return During Crash'] = (1 + XELdur['avgreturnduring']).cumprod()

#period after crash
XELaft = web.DataReader('XEL', 'yahoo', crashend, end)
XELaft['avgreturnafter'] = XELaft['Close'].pct_change(1)
XELaft['Cumulative Return After Crash'] = (1 + XELaft['avgreturnafter']).cumprod()
In [645]:
#KDE Plot for Utilities Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
AES['returns'].plot(kind = 'kde', label ='AES', figsize = (10,8), alpha=0.5, title = 'KDE of Utilities Stocks')
LNT['returns'].plot(kind = 'kde', label ='LNT', alpha=0.5)
AEE['returns'].plot(kind = 'kde', label ='AEE', alpha=0.5)
AEP['returns'].plot(kind = 'kde', label ='AEP', alpha=0.5)
AWK['returns'].plot(kind = 'kde', label ='AWK', alpha=0.5)
ATO['returns'].plot(kind = 'kde', label ='ATO', alpha=0.5)
CNP['returns'].plot(kind = 'kde', label ='CNP', alpha=0.5)
CMS['returns'].plot(kind = 'kde', label ='CMS', alpha=0.5)
ED['returns'].plot(kind = 'kde', label ='ED', alpha=0.5)
D['returns'].plot(kind = 'kde', label ='D', alpha=0.5)
DTE['returns'].plot(kind = 'kde', label ='DTE', alpha=0.5)
DUK['returns'].plot(kind = 'kde', label ='DUK', alpha=0.5)
EIX['returns'].plot(kind = 'kde', label ='EIX', alpha=0.5)
ETR['returns'].plot(kind = 'kde', label ='ETR', alpha=0.5)
EVRG['returns'].plot(kind = 'kde', label = 'EVRG', alpha=0.5)
ES['returns'].plot(kind = 'kde', label ='ES', alpha=0.5)
EXC['returns'].plot(kind = 'kde', label ='EXC', alpha=0.5)
FE['returns'].plot(kind = 'kde', label ='FE', alpha=0.5)
NEE['returns'].plot(kind = 'kde', label ='NEE', alpha=0.5)
NI['returns'].plot(kind = 'kde', label ='NI', alpha=0.5)
NRG['returns'].plot(kind = 'kde', label ='NRG', alpha=0.5)
PNW['returns'].plot(kind = 'kde', label ='PNW', alpha=0.5)
PPL['returns'].plot(kind = 'kde', label ='PPL', alpha=0.5)
PEG['returns'].plot(kind = 'kde', label ='PEG', alpha=0.5)
SRE['returns'].plot(kind = 'kde', label ='SRE', alpha=0.5)
SO['returns'].plot(kind = 'kde', label ='SO', alpha=0.5)
WEC['returns'].plot(kind = 'kde', label ='WEC', alpha=0.5)
XEL['returns'].plot(kind = 'kde', label ='XEL', alpha=0.5)

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [646]:
#Boxplots comparing returns for Utilities Sector
box_df = pd.concat([sp['returns'],AES['returns'],LNT['returns'],AEE['returns'],AEP['returns'],AWK['returns'],ATO['returns'],CNP['returns'],CMS['returns'],ED['returns'],D['returns'],DTE['returns'],DUK['returns'],EIX['returns'],ETR['returns'],EVRG['returns'],ES['returns'],EXC['returns'],FE['returns'],NEE['returns'],NI['returns'],NRG['returns'],PNW['returns'],PPL['returns'],PEG['returns'],SRE['returns'],SO['returns'],WEC['returns'],XEL['returns']], axis=1)
box_df.columns = ['S&P 500','AES','LNT','AEE','AEP','AWK','ATO','CNP','CMS','ED','D','DTE','DUK','EIX','ETR','EVRG','ES','EXC','FE','NEE','NI','NRG','PNW','PPL','PEG','SRE','SO','WEC','XEL']
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Utilities Sector Returns')
Out[646]:
<matplotlib.axes._subplots.AxesSubplot at 0x13fa60b7cf8>
In [647]:
#Cumulative Returns of Utilities Stocks for Whole period

sp['Cumulative Return'].plot(label="S&P 500")
AES['Cumulative Return'].plot(label='AES', figsize=(16,8), title = 'Cumulative Return of Utilities Stocks')
LNT['Cumulative Return'].plot(label="LNT")
AEE['Cumulative Return'].plot(label="AEE")
AEP['Cumulative Return'].plot(label="AEP")
AWK['Cumulative Return'].plot(label="AWK")
ATO['Cumulative Return'].plot(label="ATO")
CNP['Cumulative Return'].plot(label="CNP")
CMS['Cumulative Return'].plot(label="CMS")
ED['Cumulative Return'].plot(label="ED")
D['Cumulative Return'].plot(label="D")
DTE['Cumulative Return'].plot(label="DTE")
DUK['Cumulative Return'].plot(label="DUK")
EIX['Cumulative Return'].plot(label="EIX")
ETR['Cumulative Return'].plot(label="ETR")
EVRG['Cumulative Return'].plot(label="EVRG")
ES['Cumulative Return'].plot(label="ES")
EXC['Cumulative Return'].plot(label="EXC")
FE['Cumulative Return'].plot(label="FE")
NEE['Cumulative Return'].plot(label="NEE")
NI['Cumulative Return'].plot(label="NI")
NRG['Cumulative Return'].plot(label="NRG")
PNW['Cumulative Return'].plot(label="PNW")
PPL['Cumulative Return'].plot(label="PPL")
PEG['Cumulative Return'].plot(label="PEG")
SRE['Cumulative Return'].plot(label="SRE")
SO['Cumulative Return'].plot(label="SO")
WEC['Cumulative Return'].plot(label="WEC")
XEL['Cumulative Return'].plot(label="XEL")

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [648]:
#Sorting by Cumulative Return

cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],AES['Cumulative Return'].iloc[-1],LNT['Cumulative Return'].iloc[-1],AEE['Cumulative Return'].iloc[-1],AEP['Cumulative Return'].iloc[-1],AWK['Cumulative Return'].iloc[-1],ATO['Cumulative Return'].iloc[-1],CNP['Cumulative Return'].iloc[-1],CMS['Cumulative Return'].iloc[-1],ED['Cumulative Return'].iloc[-1],D['Cumulative Return'].iloc[-1],DTE['Cumulative Return'].iloc[-1],DUK['Cumulative Return'].iloc[-1],EIX['Cumulative Return'].iloc[-1],ETR['Cumulative Return'].iloc[-1],EVRG['Cumulative Return'].iloc[-1],ES['Cumulative Return'].iloc[-1],EXC['Cumulative Return'].iloc[-1],FE['Cumulative Return'].iloc[-1],NEE['Cumulative Return'].iloc[-1],NI['Cumulative Return'].iloc[-1],NRG['Cumulative Return'].iloc[-1],PNW['Cumulative Return'].iloc[-1],PPL['Cumulative Return'].iloc[-1],PEG['Cumulative Return'].iloc[-1],SRE['Cumulative Return'].iloc[-1],SO['Cumulative Return'].iloc[-1],WEC['Cumulative Return'].iloc[-1],XEL['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],AESbef['Cumulative Return Before Crash'].iloc[-1],LNTbef['Cumulative Return Before Crash'].iloc[-1],AEEbef['Cumulative Return Before Crash'].iloc[-1],AEPbef['Cumulative Return Before Crash'].iloc[-1],AWKbef['Cumulative Return Before Crash'].iloc[-1],ATObef['Cumulative Return Before Crash'].iloc[-1],CNPbef['Cumulative Return Before Crash'].iloc[-1],CMSbef['Cumulative Return Before Crash'].iloc[-1],EDbef['Cumulative Return Before Crash'].iloc[-1],Dbef['Cumulative Return Before Crash'].iloc[-1],DTEbef['Cumulative Return Before Crash'].iloc[-1],DUKbef['Cumulative Return Before Crash'].iloc[-1],EIXbef['Cumulative Return Before Crash'].iloc[-1],ETRbef['Cumulative Return Before Crash'].iloc[-1],EVRGbef['Cumulative Return Before Crash'].iloc[-1],ESbef['Cumulative Return Before Crash'].iloc[-1],EXCbef['Cumulative Return Before Crash'].iloc[-1],FEbef['Cumulative Return Before Crash'].iloc[-1],NEEbef['Cumulative Return Before Crash'].iloc[-1],NIbef['Cumulative Return Before Crash'].iloc[-1],NRGbef['Cumulative Return Before Crash'].iloc[-1],PNWbef['Cumulative Return Before Crash'].iloc[-1],PPLbef['Cumulative Return Before Crash'].iloc[-1],PEGbef['Cumulative Return Before Crash'].iloc[-1],SREbef['Cumulative Return Before Crash'].iloc[-1],SObef['Cumulative Return Before Crash'].iloc[-1],WECbef['Cumulative Return Before Crash'].iloc[-1],XELbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],AESdur['Cumulative Return During Crash'].iloc[-1],LNTdur['Cumulative Return During Crash'].iloc[-1],AEEdur['Cumulative Return During Crash'].iloc[-1],AEPdur['Cumulative Return During Crash'].iloc[-1],AWKdur['Cumulative Return During Crash'].iloc[-1],ATOdur['Cumulative Return During Crash'].iloc[-1],CNPdur['Cumulative Return During Crash'].iloc[-1],CMSdur['Cumulative Return During Crash'].iloc[-1],EDdur['Cumulative Return During Crash'].iloc[-1],Ddur['Cumulative Return During Crash'].iloc[-1],DTEdur['Cumulative Return During Crash'].iloc[-1],DUKdur['Cumulative Return During Crash'].iloc[-1],EIXdur['Cumulative Return During Crash'].iloc[-1],ETRdur['Cumulative Return During Crash'].iloc[-1],EVRGdur['Cumulative Return During Crash'].iloc[-1],ESdur['Cumulative Return During Crash'].iloc[-1],EXCdur['Cumulative Return During Crash'].iloc[-1],FEdur['Cumulative Return During Crash'].iloc[-1],NEEdur['Cumulative Return During Crash'].iloc[-1],NIdur['Cumulative Return During Crash'].iloc[-1],NRGdur['Cumulative Return During Crash'].iloc[-1],PNWdur['Cumulative Return During Crash'].iloc[-1],PPLdur['Cumulative Return During Crash'].iloc[-1],PEGdur['Cumulative Return During Crash'].iloc[-1],SREdur['Cumulative Return During Crash'].iloc[-1],SOdur['Cumulative Return During Crash'].iloc[-1],WECdur['Cumulative Return During Crash'].iloc[-1],XELdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],AESaft['Cumulative Return After Crash'].iloc[-1],LNTaft['Cumulative Return After Crash'].iloc[-1],AEEaft['Cumulative Return After Crash'].iloc[-1],AEPaft['Cumulative Return After Crash'].iloc[-1],AWKaft['Cumulative Return After Crash'].iloc[-1],ATOaft['Cumulative Return After Crash'].iloc[-1],CNPaft['Cumulative Return After Crash'].iloc[-1],CMSaft['Cumulative Return After Crash'].iloc[-1],EDaft['Cumulative Return After Crash'].iloc[-1],Daft['Cumulative Return After Crash'].iloc[-1],DTEaft['Cumulative Return After Crash'].iloc[-1],DUKaft['Cumulative Return After Crash'].iloc[-1],EIXaft['Cumulative Return After Crash'].iloc[-1],ETRaft['Cumulative Return After Crash'].iloc[-1],EVRGaft['Cumulative Return After Crash'].iloc[-1],ESaft['Cumulative Return After Crash'].iloc[-1],EXCaft['Cumulative Return After Crash'].iloc[-1],FEaft['Cumulative Return After Crash'].iloc[-1],NEEaft['Cumulative Return After Crash'].iloc[-1],NIaft['Cumulative Return After Crash'].iloc[-1],NRGaft['Cumulative Return After Crash'].iloc[-1],PNWaft['Cumulative Return After Crash'].iloc[-1],PPLaft['Cumulative Return After Crash'].iloc[-1],PEGaft['Cumulative Return After Crash'].iloc[-1],SREaft['Cumulative Return After Crash'].iloc[-1],SOaft['Cumulative Return After Crash'].iloc[-1],WECaft['Cumulative Return After Crash'].iloc[-1],XELaft['Cumulative Return After Crash'].iloc[-1]]}                         

udf2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','AES','LNT','AEE','AEP','AWK','ATO','CNP','CMS','ED','D','DTE','DUK','EIX','ETR','EVRG','ES','EXC','FE','NEE','NI','NRG','PNW','PPL','PEG','SRE','SO','WEC','XEL'])

pd.DataFrame(udf2)
Out[648]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
AES 0.756614 1.100529 0.570673 1.204718
LNT 0.952417 1.120369 0.671311 1.266317
AEE 1.016407 1.151963 0.716437 1.231546
AEP 0.929154 1.112463 0.683727 1.221571
AWK 1.074047 1.120908 0.706992 1.355311
ATO 0.977007 1.114403 0.675166 1.298509
CNP 0.738269 1.040453 0.470451 1.508264
CMS 0.963215 1.111489 0.707246 1.225314
ED 0.868057 1.089882 0.690949 1.152716
D 1.024802 1.085106 0.665434 1.419263
DTE 0.917053 1.069376 0.573423 1.495508
DUK 1.006071 1.140304 0.632456 1.395012
EIX 0.857465 1.089014 0.575142 1.369013
ETR 0.878249 1.112946 0.594751 1.326810
EVRG 0.996838 1.162820 0.610658 1.403829
ES 1.042159 1.152132 0.650601 1.390326
EXC 0.906152 1.119219 0.601404 1.346231
FE 0.897904 1.074704 0.640857 1.303706
NEE 1.085888 1.182575 0.655741 1.400308
NI 0.946415 1.133962 0.694176 1.202301
NRG 0.929150 0.987854 0.567623 1.657040
PNW 0.913043 1.162756 0.622317 1.261803
PPL 0.869213 1.024595 0.524993 1.615923
PEG 0.907517 1.004242 0.622846 1.450895
SRE 0.906559 1.083412 0.584277 1.432134
SO 0.932366 1.113575 0.619252 1.352070
WEC 1.042205 1.149287 0.692353 1.309776
XEL 1.052411 1.139655 0.707514 1.305200
In [649]:
udf2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[649]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
NRG 0.929150 0.987854 0.567623 1.657040
PPL 0.869213 1.024595 0.524993 1.615923
CNP 0.738269 1.040453 0.470451 1.508264
DTE 0.917053 1.069376 0.573423 1.495508
PEG 0.907517 1.004242 0.622846 1.450895
SRE 0.906559 1.083412 0.584277 1.432134
S&P 500 1.026077 1.083678 0.663281 1.427519
D 1.024802 1.085106 0.665434 1.419263
EVRG 0.996838 1.162820 0.610658 1.403829
NEE 1.085888 1.182575 0.655741 1.400308
DUK 1.006071 1.140304 0.632456 1.395012
ES 1.042159 1.152132 0.650601 1.390326
EIX 0.857465 1.089014 0.575142 1.369013
AWK 1.074047 1.120908 0.706992 1.355311
SO 0.932366 1.113575 0.619252 1.352070
EXC 0.906152 1.119219 0.601404 1.346231
ETR 0.878249 1.112946 0.594751 1.326810
WEC 1.042205 1.149287 0.692353 1.309776
XEL 1.052411 1.139655 0.707514 1.305200
FE 0.897904 1.074704 0.640857 1.303706
ATO 0.977007 1.114403 0.675166 1.298509
LNT 0.952417 1.120369 0.671311 1.266317
PNW 0.913043 1.162756 0.622317 1.261803
AEE 1.016407 1.151963 0.716437 1.231546
CMS 0.963215 1.111489 0.707246 1.225314
AEP 0.929154 1.112463 0.683727 1.221571
AES 0.756614 1.100529 0.570673 1.204718
NI 0.946415 1.133962 0.694176 1.202301
ED 0.868057 1.089882 0.690949 1.152716
In [650]:
#Table of Key Statistics of Utilities Stocks
import pandas as pd
import statistics as stats

umeans = [AESmean,LNTmean,AEEmean,AEPmean,AWKmean,ATOmean,CNPmean,CMSmean,EDmean,Dmean,DTEmean,DUKmean,EIXmean,ETRmean,EVRGmean,ESmean,EXCmean,FEmean,NEEmean,NImean,NRGmean,PNWmean,PPLmean,PEGmean,SREmean,SOmean,WECmean,XELmean]
uaveragemean = stats.mean(umeans)
uvariances = [AESvar,LNTvar,AEEvar,AEPvar,AWKvar,ATOvar,CNPvar,CMSvar,EDvar,Dvar,DTEvar,DUKvar,EIXvar,ETRvar,EVRGvar,ESvar,EXCvar,FEvar,NEEvar,NIvar,NRGvar,PNWvar,PPLvar,PEGvar,SREvar,SOvar,WECvar,XELvar]
uaveragevar = stats.mean(uvariances)
ukurtoses = [AESkurt,LNTkurt,AEEkurt,AEPkurt,AWKkurt,ATOkurt,CNPkurt,CMSkurt,EDkurt,Dkurt,DTEkurt,DUKkurt,EIXkurt,ETRkurt,EVRGkurt,ESkurt,EXCkurt,FEkurt,NEEkurt,NIkurt,NRGkurt,PNWkurt,PPLkurt,PEGkurt,SREkurt,SOkurt,WECkurt,XELkurt]
uaveragekurt = stats.mean(ukurtoses)
uskews = [AESskew,LNTskew,AEEskew,AEPskew,AWKskew,ATOskew,CNPskew,CMSskew,EDskew,Dskew,DTEskew,DUKskew,EIXskew,ETRskew,EVRGskew,ESskew,EXCskew,FEskew,NEEskew,NIskew,NRGskew,PNWskew,PPLskew,PEGskew,SREskew,SOskew,WECskew,XELskew]
uaverageskew = stats.mean(uskews)

Utilities = {'Mean': [[spmean],[uaveragemean],[AESmean], [LNTmean],[AEEmean],[AEPmean],[AWKmean],[ATOmean],[CNPmean],[CMSmean],[EDmean],[Dmean],[DTEmean],[DUKmean],[EIXmean],[ETRmean],[EVRGmean],[ESmean],[EXCmean],[FEmean],[NEEmean],[NImean],[NRGmean],[PNWmean],[PPLmean],[PEGmean],[SREmean],[SOmean],[WECmean],[XELmean]], 
        'Variance': [[spvar],[uaveragevar],[AESvar],[LNTvar],[AEEvar],[AEPvar],[AWKvar],[ATOvar],[CNPvar],[CMSvar],[EDvar],[Dvar],[DTEvar],[DUKvar],[EIXvar],[ETRvar],[EVRGvar],[ESvar],[EXCvar],[FEvar],[NEEvar],[NIvar],[NRGvar],[PNWvar],[PPLvar],[PEGvar],[SREvar],[SOvar],[WECvar],[XELvar]], 
          'Kurtosis': [[spkurt],[uaveragekurt],[AESkurt],[LNTkurt],[AEEkurt],[AEPkurt],[AWKkurt],[ATOkurt],[CNPkurt],[CMSkurt],[EDkurt],[Dkurt],[DTEkurt],[DUKkurt],[EIXkurt],[ETRkurt],[EVRGkurt],[ESkurt],[EXCkurt],[FEkurt],[NEEkurt],[NIkurt],[NRGkurt],[PNWkurt],[PPLkurt],[PEGkurt],[SREkurt],[SOkurt],[WECkurt],[XELkurt]],
          'Skew': [[spskew],[uaverageskew],[AESskew],[LNTskew],[AEEskew],[AEPskew],[AWKskew],[ATOskew],[CNPskew],[CMSskew],[EDskew],[Dskew],[DTEskew],[DUKskew],[EIXskew],[ETRskew],[EVRGskew],[ESskew],[EXCskew],[FEskew],[NEEskew],[NIskew],[NRGskew],[PNWskew],[PPLskew],[PEGskew],[SREskew],[SOskew],[WECskew],[XELskew]],
        }

udf = pd.DataFrame(Utilities, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','AES','LNT','AEE','AEP','AWK','ATO','CNP','CMS','ED','D','DTE','DUK','EIX','ETR','EVRG','ES','EXC','FE','NEE','NI','NRG','PNW','PPL','PEG','SRE','SO','WEC','XEL']
)

pd.DataFrame(udf)
Out[650]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [0.00016596619104204964] [0.0013407448831021097] [5.209679271543116] [-0.0035316127438560527]
AES [-0.0011232495505424528] [0.0021394892715851277] [3.3129997602724313] [-0.5757563224115685]
LNT [9.882359309215308e-05] [0.0009799920978188882] [2.132255199383441] [0.14424106668986905]
AEE [0.0007681720135003067] [0.0012876300015785588] [6.822839348515097] [0.18510665024879866]
AEP [-0.00010597241746834212] [0.0009616418743343372] [4.384535491383117] [0.15602576754152742]
AWK [0.0011970835456834976] [0.0012739043022209774] [5.048650100099256] [0.26981615182763535]
ATO [0.00037347747319579596] [0.0011336855639802255] [5.159358726108159] [0.5093038265998421]
CNP [-0.0012315863514813393] [0.0023099295714507913] [5.448819823181792] [-0.43800392102383495]
CMS [0.00020782576826855912] [0.0010177886300788844] [4.3687203596658275] [0.11342159530125649]
ED [-0.0005687183056619038] [0.0011215931067148576] [8.844862896978228] [0.5101899457661978]
D [0.0008628483165483271] [0.0013530379328426352] [5.784424215001654] [0.3507945232909957]
DTE [7.027704936934502e-05] [0.0015030015762819325] [3.735158510102379] [-0.4364884862330107]
DUK [0.0005993696618395403] [0.0011118245127634341] [3.872481493446603] [0.1416964690262782]
EIX [-0.0006145790403563351] [0.0012121372831112888] [4.250226651552681] [-0.07898728883565333]
ETR [-0.0003040664628610904] [0.0014482549357482375] [5.158085416694603] [-0.1799982991079439]
EVRG [0.000766880504620814] [0.0015893271690044057] [5.988605159695582] [0.044014004425230145]
ES [0.0010345637152522338] [0.0014312238377373072] [4.192061812869281] [0.3202903491028543]
EXC [8.97229066611238e-06] [0.0016030526799640594] [6.293252730634177] [0.45935105179310476]
FE [-0.0002120962609445378] [0.0012898818610442863] [7.516034491184173] [0.11542528738406614]
NEE [0.0012173426172515545] [0.0011347715059580466] [4.63766579066809] [0.09768058562774859]
NI [0.0002121242195972148] [0.0012931848646790603] [8.15477844265758] [-0.23098019320072896]
NRG [0.0001311353633864224] [0.0014379318375875793] [4.46949544712744] [0.1351715866627052]
PNW [-8.452237696421955e-05] [0.001261155284419754] [6.171496052456351] [-0.5557236638208279]
PPL [-0.00040579230167604486] [0.0014182366862803856] [3.3537724418978927] [0.032471786364163195]
PEG [-0.0002734743908833708] [0.0009965361568992478] [2.661733824739006] [-0.07006836638370004]
SRE [-5.330141194498901e-05] [0.0014258496946562176] [5.923345527508835] [-0.755058260117275]
SO [0.00017362070310968555] [0.0014918898025344889] [5.974231942474989] [0.7898259181153006]
WEC [0.0009788208647051868] [0.0012826889608699697] [7.6091980257362986] [-0.7208063211188428]
XEL [0.0009230745198752662] [0.001031215724714085] [4.601929921172286] [-0.4318406003421571]
In [651]:
udf.sort_values("Mean", ascending=False)
Out[651]:
Mean Variance Kurtosis Skew
NEE [0.0012173426172515545] [0.0011347715059580466] [4.63766579066809] [0.09768058562774859]
AWK [0.0011970835456834976] [0.0012739043022209774] [5.048650100099256] [0.26981615182763535]
ES [0.0010345637152522338] [0.0014312238377373072] [4.192061812869281] [0.3202903491028543]
WEC [0.0009788208647051868] [0.0012826889608699697] [7.6091980257362986] [-0.7208063211188428]
XEL [0.0009230745198752662] [0.001031215724714085] [4.601929921172286] [-0.4318406003421571]
D [0.0008628483165483271] [0.0013530379328426352] [5.784424215001654] [0.3507945232909957]
AEE [0.0007681720135003067] [0.0012876300015785588] [6.822839348515097] [0.18510665024879866]
EVRG [0.000766880504620814] [0.0015893271690044057] [5.988605159695582] [0.044014004425230145]
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
DUK [0.0005993696618395403] [0.0011118245127634341] [3.872481493446603] [0.1416964690262782]
ATO [0.00037347747319579596] [0.0011336855639802255] [5.159358726108159] [0.5093038265998421]
NI [0.0002121242195972148] [0.0012931848646790603] [8.15477844265758] [-0.23098019320072896]
CMS [0.00020782576826855912] [0.0010177886300788844] [4.3687203596658275] [0.11342159530125649]
SO [0.00017362070310968555] [0.0014918898025344889] [5.974231942474989] [0.7898259181153006]
Average [0.00016596619104204964] [0.0013407448831021097] [5.209679271543116] [-0.0035316127438560527]
NRG [0.0001311353633864224] [0.0014379318375875793] [4.46949544712744] [0.1351715866627052]
LNT [9.882359309215308e-05] [0.0009799920978188882] [2.132255199383441] [0.14424106668986905]
DTE [7.027704936934502e-05] [0.0015030015762819325] [3.735158510102379] [-0.4364884862330107]
EXC [8.97229066611238e-06] [0.0016030526799640594] [6.293252730634177] [0.45935105179310476]
SRE [-5.330141194498901e-05] [0.0014258496946562176] [5.923345527508835] [-0.755058260117275]
PNW [-8.452237696421955e-05] [0.001261155284419754] [6.171496052456351] [-0.5557236638208279]
AEP [-0.00010597241746834212] [0.0009616418743343372] [4.384535491383117] [0.15602576754152742]
FE [-0.0002120962609445378] [0.0012898818610442863] [7.516034491184173] [0.11542528738406614]
PEG [-0.0002734743908833708] [0.0009965361568992478] [2.661733824739006] [-0.07006836638370004]
ETR [-0.0003040664628610904] [0.0014482549357482375] [5.158085416694603] [-0.1799982991079439]
PPL [-0.00040579230167604486] [0.0014182366862803856] [3.3537724418978927] [0.032471786364163195]
ED [-0.0005687183056619038] [0.0011215931067148576] [8.844862896978228] [0.5101899457661978]
EIX [-0.0006145790403563351] [0.0012121372831112888] [4.250226651552681] [-0.07898728883565333]
AES [-0.0011232495505424528] [0.0021394892715851277] [3.3129997602724313] [-0.5757563224115685]
CNP [-0.0012315863514813393] [0.0023099295714507913] [5.448819823181792] [-0.43800392102383495]

Real Estate

In [652]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ARE = web.DataReader('ARE', 'yahoo', start, end)
ARE['returns'] = ARE['Close'].pct_change(1)
ARE['Cumulative Return'] = (1 + ARE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AREmean = ARE['returns'].mean()
AREvar = ARE['returns'].var()
AREkurt = ARE['returns'].kurt()
AREskew = ARE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AREbef = web.DataReader('ARE', 'yahoo', start, crashstart)
AREbef['avgreturnbefore'] = AREbef['Close'].pct_change(1)
AREbef['Cumulative Return Before Crash'] = (1 + AREbef['avgreturnbefore']).cumprod()

#period of crash
AREdur = web.DataReader('ARE', 'yahoo', crashstart, crashend)
AREdur['avgreturnduring'] = AREdur['Close'].pct_change(1)
AREdur['Cumulative Return During Crash'] = (1 + AREdur['avgreturnduring']).cumprod()

#period after crash
AREaft = web.DataReader('ARE', 'yahoo', crashend, end)
AREaft['avgreturnafter'] = AREaft['Close'].pct_change(1)
AREaft['Cumulative Return After Crash'] = (1 + AREaft['avgreturnafter']).cumprod()
In [653]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AMT = web.DataReader('AMT', 'yahoo', start, end)
AMT['returns'] = AMT['Close'].pct_change(1)
AMT['Cumulative Return'] = (1 + AMT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AMTmean = AMT['returns'].mean()
AMTvar = AMT['returns'].var()
AMTkurt = AMT['returns'].kurt()
AMTskew = AMT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AMTbef = web.DataReader('AMT', 'yahoo', start, crashstart)
AMTbef['avgreturnbefore'] = AMTbef['Close'].pct_change(1)
AMTbef['Cumulative Return Before Crash'] = (1 + AMTbef['avgreturnbefore']).cumprod()

#period of crash
AMTdur = web.DataReader('AMT', 'yahoo', crashstart, crashend)
AMTdur['avgreturnduring'] = AMTdur['Close'].pct_change(1)
AMTdur['Cumulative Return During Crash'] = (1 + AMTdur['avgreturnduring']).cumprod()

#period after crash
AMTaft = web.DataReader('AMT', 'yahoo', crashend, end)
AMTaft['avgreturnafter'] = AMTaft['Close'].pct_change(1)
AMTaft['Cumulative Return After Crash'] = (1 + AMTaft['avgreturnafter']).cumprod()
In [654]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AIV = web.DataReader('AIV', 'yahoo', start, end)
AIV['returns'] = AIV['Close'].pct_change(1)
AIV['Cumulative Return'] = (1 + AIV['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AIVmean = AIV['returns'].mean()
AIVvar = AIV['returns'].var()
AIVkurt = AIV['returns'].kurt()
AIVskew = AIV['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AIVbef = web.DataReader('AIV', 'yahoo', start, crashstart)
AIVbef['avgreturnbefore'] = AIVbef['Close'].pct_change(1)
AIVbef['Cumulative Return Before Crash'] = (1 + AIVbef['avgreturnbefore']).cumprod()

#period of crash
AIVdur = web.DataReader('AIV', 'yahoo', crashstart, crashend)
AIVdur['avgreturnduring'] = AIVdur['Close'].pct_change(1)
AIVdur['Cumulative Return During Crash'] = (1 + AIVdur['avgreturnduring']).cumprod()

#period after crash
AIVaft = web.DataReader('AIV', 'yahoo', crashend, end)
AIVaft['avgreturnafter'] = AIVaft['Close'].pct_change(1)
AIVaft['Cumulative Return After Crash'] = (1 + AIVaft['avgreturnafter']).cumprod()
In [655]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
AVB = web.DataReader('AVB', 'yahoo', start, end)
AVB['returns'] = AVB['Close'].pct_change(1)
AVB['Cumulative Return'] = (1 + AVB['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
AVBmean = AVB['returns'].mean()
AVBvar = AVB['returns'].var()
AVBkurt = AVB['returns'].kurt()
AVBskew = AVB['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
AVBbef = web.DataReader('AVB', 'yahoo', start, crashstart)
AVBbef['avgreturnbefore'] = AVBbef['Close'].pct_change(1)
AVBbef['Cumulative Return Before Crash'] = (1 + AVBbef['avgreturnbefore']).cumprod()

#period of crash
AVBdur = web.DataReader('AVB', 'yahoo', crashstart, crashend)
AVBdur['avgreturnduring'] = AVBdur['Close'].pct_change(1)
AVBdur['Cumulative Return During Crash'] = (1 + AVBdur['avgreturnduring']).cumprod()

#period after crash
AVBaft = web.DataReader('AVB', 'yahoo', crashend, end)
AVBaft['avgreturnafter'] = AVBaft['Close'].pct_change(1)
AVBaft['Cumulative Return After Crash'] = (1 + AVBaft['avgreturnafter']).cumprod()
In [656]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
BXP = web.DataReader('BXP', 'yahoo', start, end)
BXP['returns'] = BXP['Close'].pct_change(1)
BXP['Cumulative Return'] = (1 + BXP['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
BXPmean = BXP['returns'].mean()
BXPvar = BXP['returns'].var()
BXPkurt = BXP['returns'].kurt()
BXPskew = BXP['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
BXPbef = web.DataReader('BXP', 'yahoo', start, crashstart)
BXPbef['avgreturnbefore'] = BXPbef['Close'].pct_change(1)
BXPbef['Cumulative Return Before Crash'] = (1 + BXPbef['avgreturnbefore']).cumprod()

#period of crash
BXPdur = web.DataReader('BXP', 'yahoo', crashstart, crashend)
BXPdur['avgreturnduring'] = BXPdur['Close'].pct_change(1)
BXPdur['Cumulative Return During Crash'] = (1 + BXPdur['avgreturnduring']).cumprod()

#period after crash
BXPaft = web.DataReader('BXP', 'yahoo', crashend, end)
BXPaft['avgreturnafter'] = BXPaft['Close'].pct_change(1)
BXPaft['Cumulative Return After Crash'] = (1 + BXPaft['avgreturnafter']).cumprod()
In [657]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CBRE = web.DataReader('CBRE', 'yahoo', start, end)
CBRE['returns'] = CBRE['Close'].pct_change(1)
CBRE['Cumulative Return'] = (1 + CBRE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CBREmean = CBRE['returns'].mean()
CBREvar = CBRE['returns'].var()
CBREkurt = CBRE['returns'].kurt()
CBREskew = CBRE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CBREbef = web.DataReader('CBRE', 'yahoo', start, crashstart)
CBREbef['avgreturnbefore'] = CBREbef['Close'].pct_change(1)
CBREbef['Cumulative Return Before Crash'] = (1 + CBREbef['avgreturnbefore']).cumprod()

#period of crash
CBREdur = web.DataReader('CBRE', 'yahoo', crashstart, crashend)
CBREdur['avgreturnduring'] = CBREdur['Close'].pct_change(1)
CBREdur['Cumulative Return During Crash'] = (1 + CBREdur['avgreturnduring']).cumprod()

#period after crash
CBREaft = web.DataReader('CBRE', 'yahoo', crashend, end)
CBREaft['avgreturnafter'] = CBREaft['Close'].pct_change(1)
CBREaft['Cumulative Return After Crash'] = (1 + CBREaft['avgreturnafter']).cumprod()
In [658]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
CCI = web.DataReader('CCI', 'yahoo', start, end)
CCI['returns'] = CCI['Close'].pct_change(1)
CCI['Cumulative Return'] = (1 + CCI['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
CCImean = CCI['returns'].mean()
CCIvar = CCI['returns'].var()
CCIkurt = CCI['returns'].kurt()
CCIskew = CCI['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
CCIbef = web.DataReader('CCI', 'yahoo', start, crashstart)
CCIbef['avgreturnbefore'] = CCIbef['Close'].pct_change(1)
CCIbef['Cumulative Return Before Crash'] = (1 + CCIbef['avgreturnbefore']).cumprod()

#period of crash
CCIdur = web.DataReader('CCI', 'yahoo', crashstart, crashend)
CCIdur['avgreturnduring'] = CCIdur['Close'].pct_change(1)
CCIdur['Cumulative Return During Crash'] = (1 + CCIdur['avgreturnduring']).cumprod()

#period after crash
CCIaft = web.DataReader('CCI', 'yahoo', crashend, end)
CCIaft['avgreturnafter'] = CCIaft['Close'].pct_change(1)
CCIaft['Cumulative Return After Crash'] = (1 + CCIaft['avgreturnafter']).cumprod()
In [659]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DLR = web.DataReader('DLR', 'yahoo', start, end)
DLR['returns'] = DLR['Close'].pct_change(1)
DLR['Cumulative Return'] = (1 + DLR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DLRmean = DLR['returns'].mean()
DLRvar = DLR['returns'].var()
DLRkurt = DLR['returns'].kurt()
DLRskew = DLR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DLRbef = web.DataReader('DLR', 'yahoo', start, crashstart)
DLRbef['avgreturnbefore'] = DLRbef['Close'].pct_change(1)
DLRbef['Cumulative Return Before Crash'] = (1 + DLRbef['avgreturnbefore']).cumprod()

#period of crash
DLRdur = web.DataReader('DLR', 'yahoo', crashstart, crashend)
DLRdur['avgreturnduring'] = DLRdur['Close'].pct_change(1)
DLRdur['Cumulative Return During Crash'] = (1 + DLRdur['avgreturnduring']).cumprod()

#period after crash
DLRaft = web.DataReader('DLR', 'yahoo', crashend, end)
DLRaft['avgreturnafter'] = DLRaft['Close'].pct_change(1)
DLRaft['Cumulative Return After Crash'] = (1 + DLRaft['avgreturnafter']).cumprod()
In [660]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
DRE = web.DataReader('DRE', 'yahoo', start, end)
DRE['returns'] = DRE['Close'].pct_change(1)
DRE['Cumulative Return'] = (1 + DRE['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
DREmean = DRE['returns'].mean()
DREvar = DRE['returns'].var()
DREkurt = DRE['returns'].kurt()
DREskew = DRE['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
DREbef = web.DataReader('DRE', 'yahoo', start, crashstart)
DREbef['avgreturnbefore'] = DREbef['Close'].pct_change(1)
DREbef['Cumulative Return Before Crash'] = (1 + DREbef['avgreturnbefore']).cumprod()

#period of crash
DREdur = web.DataReader('DRE', 'yahoo', crashstart, crashend)
DREdur['avgreturnduring'] = DREdur['Close'].pct_change(1)
DREdur['Cumulative Return During Crash'] = (1 + DREdur['avgreturnduring']).cumprod()

#period after crash
DREaft = web.DataReader('DRE', 'yahoo', crashend, end)
DREaft['avgreturnafter'] = DREaft['Close'].pct_change(1)
DREaft['Cumulative Return After Crash'] = (1 + DREaft['avgreturnafter']).cumprod()
In [661]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EQIX = web.DataReader('EQIX', 'yahoo', start, end)
EQIX['returns'] = EQIX['Close'].pct_change(1)
EQIX['Cumulative Return'] = (1 + EQIX['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EQIXmean = EQIX['returns'].mean()
EQIXvar = EQIX['returns'].var()
EQIXkurt = EQIX['returns'].kurt()
EQIXskew = EQIX['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EQIXbef = web.DataReader('EQIX', 'yahoo', start, crashstart)
EQIXbef['avgreturnbefore'] = EQIXbef['Close'].pct_change(1)
EQIXbef['Cumulative Return Before Crash'] = (1 + EQIXbef['avgreturnbefore']).cumprod()

#period of crash
EQIXdur = web.DataReader('EQIX', 'yahoo', crashstart, crashend)
EQIXdur['avgreturnduring'] = EQIXdur['Close'].pct_change(1)
EQIXdur['Cumulative Return During Crash'] = (1 + EQIXdur['avgreturnduring']).cumprod()

#period after crash
EQIXaft = web.DataReader('EQIX', 'yahoo', crashend, end)
EQIXaft['avgreturnafter'] = EQIXaft['Close'].pct_change(1)
EQIXaft['Cumulative Return After Crash'] = (1 + EQIXaft['avgreturnafter']).cumprod()
In [662]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EQR = web.DataReader('EQR', 'yahoo', start, end)
EQR['returns'] = EQR['Close'].pct_change(1)
EQR['Cumulative Return'] = (1 + EQR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EQRmean = EQR['returns'].mean()
EQRvar = EQR['returns'].var()
EQRkurt = EQR['returns'].kurt()
EQRskew = EQR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EQRbef = web.DataReader('EQR', 'yahoo', start, crashstart)
EQRbef['avgreturnbefore'] = EQRbef['Close'].pct_change(1)
EQRbef['Cumulative Return Before Crash'] = (1 + EQRbef['avgreturnbefore']).cumprod()

#period of crash
EQRdur = web.DataReader('EQR', 'yahoo', crashstart, crashend)
EQRdur['avgreturnduring'] = EQRdur['Close'].pct_change(1)
EQRdur['Cumulative Return During Crash'] = (1 + EQRdur['avgreturnduring']).cumprod()

#period after crash
EQRaft = web.DataReader('EQR', 'yahoo', crashend, end)
EQRaft['avgreturnafter'] = EQRaft['Close'].pct_change(1)
EQRaft['Cumulative Return After Crash'] = (1 + EQRaft['avgreturnafter']).cumprod()
In [663]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
ESS = web.DataReader('ESS', 'yahoo', start, end)
ESS['returns'] = ESS['Close'].pct_change(1)
ESS['Cumulative Return'] = (1 + ESS['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
ESSmean = ESS['returns'].mean()
ESSvar = ESS['returns'].var()
ESSkurt = ESS['returns'].kurt()
ESSskew = ESS['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
ESSbef = web.DataReader('ESS', 'yahoo', start, crashstart)
ESSbef['avgreturnbefore'] = ESSbef['Close'].pct_change(1)
ESSbef['Cumulative Return Before Crash'] = (1 + ESSbef['avgreturnbefore']).cumprod()

#period of crash
ESSdur = web.DataReader('ESS', 'yahoo', crashstart, crashend)
ESSdur['avgreturnduring'] = ESSdur['Close'].pct_change(1)
ESSdur['Cumulative Return During Crash'] = (1 + ESSdur['avgreturnduring']).cumprod()

#period after crash
ESSaft = web.DataReader('ESS', 'yahoo', crashend, end)
ESSaft['avgreturnafter'] = ESSaft['Close'].pct_change(1)
ESSaft['Cumulative Return After Crash'] = (1 + ESSaft['avgreturnafter']).cumprod()
In [664]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
EXR = web.DataReader('EXR', 'yahoo', start, end)
EXR['returns'] = EXR['Close'].pct_change(1)
EXR['Cumulative Return'] = (1 + EXR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
EXRmean = EXR['returns'].mean()
EXRvar = EXR['returns'].var()
EXRkurt = EXR['returns'].kurt()
EXRskew = EXR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
EXRbef = web.DataReader('EXR', 'yahoo', start, crashstart)
EXRbef['avgreturnbefore'] = EXRbef['Close'].pct_change(1)
EXRbef['Cumulative Return Before Crash'] = (1 + EXRbef['avgreturnbefore']).cumprod()

#period of crash
EXRdur = web.DataReader('EXR', 'yahoo', crashstart, crashend)
EXRdur['avgreturnduring'] = EXRdur['Close'].pct_change(1)
EXRdur['Cumulative Return During Crash'] = (1 + EXRdur['avgreturnduring']).cumprod()

#period after crash
EXRaft = web.DataReader('EXR', 'yahoo', crashend, end)
EXRaft['avgreturnafter'] = EXRaft['Close'].pct_change(1)
EXRaft['Cumulative Return After Crash'] = (1 + EXRaft['avgreturnafter']).cumprod()
In [665]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
FRT = web.DataReader('FRT', 'yahoo', start, end)
FRT['returns'] = FRT['Close'].pct_change(1)
FRT['Cumulative Return'] = (1 + FRT['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
FRTmean = FRT['returns'].mean()
FRTvar = FRT['returns'].var()
FRTkurt = FRT['returns'].kurt()
FRTskew = FRT['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
FRTbef = web.DataReader('FRT', 'yahoo', start, crashstart)
FRTbef['avgreturnbefore'] = FRTbef['Close'].pct_change(1)
FRTbef['Cumulative Return Before Crash'] = (1 + FRTbef['avgreturnbefore']).cumprod()

#period of crash
FRTdur = web.DataReader('FRT', 'yahoo', crashstart, crashend)
FRTdur['avgreturnduring'] = FRTdur['Close'].pct_change(1)
FRTdur['Cumulative Return During Crash'] = (1 + FRTdur['avgreturnduring']).cumprod()

#period after crash
FRTaft = web.DataReader('FRT', 'yahoo', crashend, end)
FRTaft['avgreturnafter'] = FRTaft['Close'].pct_change(1)
FRTaft['Cumulative Return After Crash'] = (1 + FRTaft['avgreturnafter']).cumprod()
In [666]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PEAK = web.DataReader('PEAK', 'yahoo', start, end)
PEAK['returns'] = PEAK['Close'].pct_change(1)
PEAK['Cumulative Return'] = (1 + PEAK['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PEAKmean = PEAK['returns'].mean()
PEAKvar = PEAK['returns'].var()
PEAKkurt = PEAK['returns'].kurt()
PEAKskew = PEAK['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PEAKbef = web.DataReader('PEAK', 'yahoo', start, crashstart)
PEAKbef['avgreturnbefore'] = PEAKbef['Close'].pct_change(1)
PEAKbef['Cumulative Return Before Crash'] = (1 + PEAKbef['avgreturnbefore']).cumprod()

#period of crash
PEAKdur = web.DataReader('PEAK', 'yahoo', crashstart, crashend)
PEAKdur['avgreturnduring'] = PEAKdur['Close'].pct_change(1)
PEAKdur['Cumulative Return During Crash'] = (1 + PEAKdur['avgreturnduring']).cumprod()

#period after crash
PEAKaft = web.DataReader('PEAK', 'yahoo', crashend, end)
PEAKaft['avgreturnafter'] = PEAKaft['Close'].pct_change(1)
PEAKaft['Cumulative Return After Crash'] = (1 + PEAKaft['avgreturnafter']).cumprod()
In [667]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
HST = web.DataReader('HST', 'yahoo', start, end)
HST['returns'] = HST['Close'].pct_change(1)
HST['Cumulative Return'] = (1 + HST['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
HSTmean = HST['returns'].mean()
HSTvar = HST['returns'].var()
HSTkurt = HST['returns'].kurt()
HSTskew = HST['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
HSTbef = web.DataReader('HST', 'yahoo', start, crashstart)
HSTbef['avgreturnbefore'] = HSTbef['Close'].pct_change(1)
HSTbef['Cumulative Return Before Crash'] = (1 + HSTbef['avgreturnbefore']).cumprod()

#period of crash
HSTdur = web.DataReader('HST', 'yahoo', crashstart, crashend)
HSTdur['avgreturnduring'] = HSTdur['Close'].pct_change(1)
HSTdur['Cumulative Return During Crash'] = (1 + HSTdur['avgreturnduring']).cumprod()

#period after crash
HSTaft = web.DataReader('HST', 'yahoo', crashend, end)
HSTaft['avgreturnafter'] = HSTaft['Close'].pct_change(1)
HSTaft['Cumulative Return After Crash'] = (1 + HSTaft['avgreturnafter']).cumprod()
In [668]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
IRM = web.DataReader('IRM', 'yahoo', start, end)
IRM['returns'] = IRM['Close'].pct_change(1)
IRM['Cumulative Return'] = (1 + IRM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
IRMmean = IRM['returns'].mean()
IRMvar = IRM['returns'].var()
IRMkurt = IRM['returns'].kurt()
IRMskew = IRM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
IRMbef = web.DataReader('IRM', 'yahoo', start, crashstart)
IRMbef['avgreturnbefore'] = IRMbef['Close'].pct_change(1)
IRMbef['Cumulative Return Before Crash'] = (1 + IRMbef['avgreturnbefore']).cumprod()

#period of crash
IRMdur = web.DataReader('IRM', 'yahoo', crashstart, crashend)
IRMdur['avgreturnduring'] = IRMdur['Close'].pct_change(1)
IRMdur['Cumulative Return During Crash'] = (1 + IRMdur['avgreturnduring']).cumprod()

#period after crash
IRMaft = web.DataReader('IRM', 'yahoo', crashend, end)
IRMaft['avgreturnafter'] = IRMaft['Close'].pct_change(1)
IRMaft['Cumulative Return After Crash'] = (1 + IRMaft['avgreturnafter']).cumprod()
In [669]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
KIM = web.DataReader('KIM', 'yahoo', start, end)
KIM['returns'] = KIM['Close'].pct_change(1)
KIM['Cumulative Return'] = (1 + KIM['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
KIMmean = KIM['returns'].mean()
KIMvar = KIM['returns'].var()
KIMkurt = KIM['returns'].kurt()
KIMskew = KIM['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
KIMbef = web.DataReader('KIM', 'yahoo', start, crashstart)
KIMbef['avgreturnbefore'] = KIMbef['Close'].pct_change(1)
KIMbef['Cumulative Return Before Crash'] = (1 + KIMbef['avgreturnbefore']).cumprod()

#period of crash
KIMdur = web.DataReader('KIM', 'yahoo', crashstart, crashend)
KIMdur['avgreturnduring'] = KIMdur['Close'].pct_change(1)
KIMdur['Cumulative Return During Crash'] = (1 + KIMdur['avgreturnduring']).cumprod()

#period after crash
KIMaft = web.DataReader('KIM', 'yahoo', crashend, end)
KIMaft['avgreturnafter'] = KIMaft['Close'].pct_change(1)
KIMaft['Cumulative Return After Crash'] = (1 + KIMaft['avgreturnafter']).cumprod()
In [670]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
MAA = web.DataReader('MAA', 'yahoo', start, end)
MAA['returns'] = MAA['Close'].pct_change(1)
MAA['Cumulative Return'] = (1 + MAA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
MAAmean = MAA['returns'].mean()
MAAvar = MAA['returns'].var()
MAAkurt = MAA['returns'].kurt()
MAAskew = MAA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
MAAbef = web.DataReader('MAA', 'yahoo', start, crashstart)
MAAbef['avgreturnbefore'] = MAAbef['Close'].pct_change(1)
MAAbef['Cumulative Return Before Crash'] = (1 + MAAbef['avgreturnbefore']).cumprod()

#period of crash
MAAdur = web.DataReader('MAA', 'yahoo', crashstart, crashend)
MAAdur['avgreturnduring'] = MAAdur['Close'].pct_change(1)
MAAdur['Cumulative Return During Crash'] = (1 + MAAdur['avgreturnduring']).cumprod()

#period after crash
MAAaft = web.DataReader('MAA', 'yahoo', crashend, end)
MAAaft['avgreturnafter'] = MAAaft['Close'].pct_change(1)
MAAaft['Cumulative Return After Crash'] = (1 + MAAaft['avgreturnafter']).cumprod()
In [671]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PLD = web.DataReader('PLD', 'yahoo', start, end)
PLD['returns'] = PLD['Close'].pct_change(1)
PLD['Cumulative Return'] = (1 + PLD['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PLDmean = PLD['returns'].mean()
PLDvar = PLD['returns'].var()
PLDkurt = PLD['returns'].kurt()
PLDskew = PLD['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PLDbef = web.DataReader('PLD', 'yahoo', start, crashstart)
PLDbef['avgreturnbefore'] = PLDbef['Close'].pct_change(1)
PLDbef['Cumulative Return Before Crash'] = (1 + PLDbef['avgreturnbefore']).cumprod()

#period of crash
PLDdur = web.DataReader('PLD', 'yahoo', crashstart, crashend)
PLDdur['avgreturnduring'] = PLDdur['Close'].pct_change(1)
PLDdur['Cumulative Return During Crash'] = (1 + PLDdur['avgreturnduring']).cumprod()

#period after crash
PLDaft = web.DataReader('PLD', 'yahoo', crashend, end)
PLDaft['avgreturnafter'] = PLDaft['Close'].pct_change(1)
PLDaft['Cumulative Return After Crash'] = (1 + PLDaft['avgreturnafter']).cumprod()
In [674]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
PSA = web.DataReader('PSA', 'yahoo', start, end)
PSA['returns'] = PSA['Close'].pct_change(1)
PSA['Cumulative Return'] = (1 + PSA['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
PSAmean = PSA['returns'].mean()
PSAvar = PSA['returns'].var()
PSAkurt = PSA['returns'].kurt()
PSAskew = PSA['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
PSAbef = web.DataReader('PSA', 'yahoo', start, crashstart)
PSAbef['avgreturnbefore'] = PSAbef['Close'].pct_change(1)
PSAbef['Cumulative Return Before Crash'] = (1 + PSAbef['avgreturnbefore']).cumprod()

#period of crash
PSAdur = web.DataReader('PSA', 'yahoo', crashstart, crashend)
PSAdur['avgreturnduring'] = PSAdur['Close'].pct_change(1)
PSAdur['Cumulative Return During Crash'] = (1 + PSAdur['avgreturnduring']).cumprod()

#period after crash
PSAaft = web.DataReader('PSA', 'yahoo', crashend, end)
PSAaft['avgreturnafter'] = PSAaft['Close'].pct_change(1)
PSAaft['Cumulative Return After Crash'] = (1 + PSAaft['avgreturnafter']).cumprod()
In [675]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
O = web.DataReader('O', 'yahoo', start, end)
O['returns'] = O['Close'].pct_change(1)
O['Cumulative Return'] = (1 + O['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
Omean = O['returns'].mean()
Ovar = O['returns'].var()
Okurt = O['returns'].kurt()
Oskew = O['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
Obef = web.DataReader('O', 'yahoo', start, crashstart)
Obef['avgreturnbefore'] = Obef['Close'].pct_change(1)
Obef['Cumulative Return Before Crash'] = (1 + Obef['avgreturnbefore']).cumprod()

#period of crash
Odur = web.DataReader('O', 'yahoo', crashstart, crashend)
Odur['avgreturnduring'] = Odur['Close'].pct_change(1)
Odur['Cumulative Return During Crash'] = (1 + Odur['avgreturnduring']).cumprod()

#period after crash
Oaft = web.DataReader('O', 'yahoo', crashend, end)
Oaft['avgreturnafter'] = Oaft['Close'].pct_change(1)
Oaft['Cumulative Return After Crash'] = (1 + Oaft['avgreturnafter']).cumprod()
In [676]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
REG = web.DataReader('REG', 'yahoo', start, end)
REG['returns'] = REG['Close'].pct_change(1)
REG['Cumulative Return'] = (1 + REG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
REGmean = REG['returns'].mean()
REGvar = REG['returns'].var()
REGkurt = REG['returns'].kurt()
REGskew = REG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
REGbef = web.DataReader('REG', 'yahoo', start, crashstart)
REGbef['avgreturnbefore'] = REGbef['Close'].pct_change(1)
REGbef['Cumulative Return Before Crash'] = (1 + REGbef['avgreturnbefore']).cumprod()

#period of crash
REGdur = web.DataReader('REG', 'yahoo', crashstart, crashend)
REGdur['avgreturnduring'] = REGdur['Close'].pct_change(1)
REGdur['Cumulative Return During Crash'] = (1 + REGdur['avgreturnduring']).cumprod()

#period after crash
REGaft = web.DataReader('REG', 'yahoo', crashend, end)
REGaft['avgreturnafter'] = REGaft['Close'].pct_change(1)
REGaft['Cumulative Return After Crash'] = (1 + REGaft['avgreturnafter']).cumprod()
In [677]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SBAC = web.DataReader('SBAC', 'yahoo', start, end)
SBAC['returns'] = SBAC['Close'].pct_change(1)
SBAC['Cumulative Return'] = (1 + SBAC['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SBACmean = SBAC['returns'].mean()
SBACvar = SBAC['returns'].var()
SBACkurt = SBAC['returns'].kurt()
SBACskew = SBAC['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SBACbef = web.DataReader('SBAC', 'yahoo', start, crashstart)
SBACbef['avgreturnbefore'] = SBACbef['Close'].pct_change(1)
SBACbef['Cumulative Return Before Crash'] = (1 + SBACbef['avgreturnbefore']).cumprod()

#period of crash
SBACdur = web.DataReader('SBAC', 'yahoo', crashstart, crashend)
SBACdur['avgreturnduring'] = SBACdur['Close'].pct_change(1)
SBACdur['Cumulative Return During Crash'] = (1 + SBACdur['avgreturnduring']).cumprod()

#period after crash
SBACaft = web.DataReader('SBAC', 'yahoo', crashend, end)
SBACaft['avgreturnafter'] = SBACaft['Close'].pct_change(1)
SBACaft['Cumulative Return After Crash'] = (1 + SBACaft['avgreturnafter']).cumprod()
In [678]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SPG = web.DataReader('SPG', 'yahoo', start, end)
SPG['returns'] = SPG['Close'].pct_change(1)
SPG['Cumulative Return'] = (1 + SPG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SPGmean = SPG['returns'].mean()
SPGvar = SPG['returns'].var()
SPGkurt = SPG['returns'].kurt()
SPGskew = SPG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SPGbef = web.DataReader('SPG', 'yahoo', start, crashstart)
SPGbef['avgreturnbefore'] = SPGbef['Close'].pct_change(1)
SPGbef['Cumulative Return Before Crash'] = (1 + SPGbef['avgreturnbefore']).cumprod()

#period of crash
SPGdur = web.DataReader('SPG', 'yahoo', crashstart, crashend)
SPGdur['avgreturnduring'] = SPGdur['Close'].pct_change(1)
SPGdur['Cumulative Return During Crash'] = (1 + SPGdur['avgreturnduring']).cumprod()

#period after crash
SPGaft = web.DataReader('SPG', 'yahoo', crashend, end)
SPGaft['avgreturnafter'] = SPGaft['Close'].pct_change(1)
SPGaft['Cumulative Return After Crash'] = (1 + SPGaft['avgreturnafter']).cumprod()
In [679]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
SLG = web.DataReader('SLG', 'yahoo', start, end)
SLG['returns'] = SLG['Close'].pct_change(1)
SLG['Cumulative Return'] = (1 + SLG['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
SLGmean = SLG['returns'].mean()
SLGvar = SLG['returns'].var()
SLGkurt = SLG['returns'].kurt()
SLGskew = SLG['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
SLGbef = web.DataReader('SLG', 'yahoo', start, crashstart)
SLGbef['avgreturnbefore'] = SLGbef['Close'].pct_change(1)
SLGbef['Cumulative Return Before Crash'] = (1 + SLGbef['avgreturnbefore']).cumprod()

#period of crash
SLGdur = web.DataReader('SLG', 'yahoo', crashstart, crashend)
SLGdur['avgreturnduring'] = SLGdur['Close'].pct_change(1)
SLGdur['Cumulative Return During Crash'] = (1 + SLGdur['avgreturnduring']).cumprod()

#period after crash
SLGaft = web.DataReader('SLG', 'yahoo', crashend, end)
SLGaft['avgreturnafter'] = SLGaft['Close'].pct_change(1)
SLGaft['Cumulative Return After Crash'] = (1 + SLGaft['avgreturnafter']).cumprod()
In [680]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
UDR = web.DataReader('UDR', 'yahoo', start, end)
UDR['returns'] = UDR['Close'].pct_change(1)
UDR['Cumulative Return'] = (1 + UDR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
UDRmean = UDR['returns'].mean()
UDRvar = UDR['returns'].var()
UDRkurt = UDR['returns'].kurt()
UDRskew = UDR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
UDRbef = web.DataReader('UDR', 'yahoo', start, crashstart)
UDRbef['avgreturnbefore'] = UDRbef['Close'].pct_change(1)
UDRbef['Cumulative Return Before Crash'] = (1 + UDRbef['avgreturnbefore']).cumprod()

#period of crash
UDRdur = web.DataReader('UDR', 'yahoo', crashstart, crashend)
UDRdur['avgreturnduring'] = UDRdur['Close'].pct_change(1)
UDRdur['Cumulative Return During Crash'] = (1 + UDRdur['avgreturnduring']).cumprod()

#period after crash
UDRaft = web.DataReader('UDR', 'yahoo', crashend, end)
UDRaft['avgreturnafter'] = UDRaft['Close'].pct_change(1)
UDRaft['Cumulative Return After Crash'] = (1 + UDRaft['avgreturnafter']).cumprod()
In [681]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
VTR = web.DataReader('VTR', 'yahoo', start, end)
VTR['returns'] = VTR['Close'].pct_change(1)
VTR['Cumulative Return'] = (1 + VTR['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
VTRmean = VTR['returns'].mean()
VTRvar = VTR['returns'].var()
VTRkurt = VTR['returns'].kurt()
VTRskew = VTR['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
VTRbef = web.DataReader('VTR', 'yahoo', start, crashstart)
VTRbef['avgreturnbefore'] = VTRbef['Close'].pct_change(1)
VTRbef['Cumulative Return Before Crash'] = (1 + VTRbef['avgreturnbefore']).cumprod()

#period of crash
VTRdur = web.DataReader('VTR', 'yahoo', crashstart, crashend)
VTRdur['avgreturnduring'] = VTRdur['Close'].pct_change(1)
VTRdur['Cumulative Return During Crash'] = (1 + VTRdur['avgreturnduring']).cumprod()

#period after crash
VTRaft = web.DataReader('VTR', 'yahoo', crashend, end)
VTRaft['avgreturnafter'] = VTRaft['Close'].pct_change(1)
VTRaft['Cumulative Return After Crash'] = (1 + VTRaft['avgreturnafter']).cumprod()
In [682]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
VNO = web.DataReader('VNO', 'yahoo', start, end)
VNO['returns'] = VNO['Close'].pct_change(1)
VNO['Cumulative Return'] = (1 + VNO['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
VNOmean = VNO['returns'].mean()
VNOvar = VNO['returns'].var()
VNOkurt = VNO['returns'].kurt()
VNOskew = VNO['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
VNObef = web.DataReader('VNO', 'yahoo', start, crashstart)
VNObef['avgreturnbefore'] = VNObef['Close'].pct_change(1)
VNObef['Cumulative Return Before Crash'] = (1 + VNObef['avgreturnbefore']).cumprod()

#period of crash
VNOdur = web.DataReader('VNO', 'yahoo', crashstart, crashend)
VNOdur['avgreturnduring'] = VNOdur['Close'].pct_change(1)
VNOdur['Cumulative Return During Crash'] = (1 + VNOdur['avgreturnduring']).cumprod()

#period after crash
VNOaft = web.DataReader('VNO', 'yahoo', crashend, end)
VNOaft['avgreturnafter'] = VNOaft['Close'].pct_change(1)
VNOaft['Cumulative Return After Crash'] = (1 + VNOaft['avgreturnafter']).cumprod()
In [683]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WELL = web.DataReader('WELL', 'yahoo', start, end)
WELL['returns'] = WELL['Close'].pct_change(1)
WELL['Cumulative Return'] = (1 + WELL['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WELLmean = WELL['returns'].mean()
WELLvar = WELL['returns'].var()
WELLkurt = WELL['returns'].kurt()
WELLskew = WELL['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WELLbef = web.DataReader('WELL', 'yahoo', start, crashstart)
WELLbef['avgreturnbefore'] = WELLbef['Close'].pct_change(1)
WELLbef['Cumulative Return Before Crash'] = (1 + WELLbef['avgreturnbefore']).cumprod()

#period of crash
WELLdur = web.DataReader('WELL', 'yahoo', crashstart, crashend)
WELLdur['avgreturnduring'] = WELLdur['Close'].pct_change(1)
WELLdur['Cumulative Return During Crash'] = (1 + WELLdur['avgreturnduring']).cumprod()

#period after crash
WELLaft = web.DataReader('WELL', 'yahoo', crashend, end)
WELLaft['avgreturnafter'] = WELLaft['Close'].pct_change(1)
WELLaft['Cumulative Return After Crash'] = (1 + WELLaft['avgreturnafter']).cumprod()
In [684]:
start = datetime.datetime(2019, 12, 4)
end = datetime.datetime(2020, 6, 5)
WY = web.DataReader('WY', 'yahoo', start, end)
WY['returns'] = WY['Close'].pct_change(1)
WY['Cumulative Return'] = (1 + WY['returns']).cumprod()

#Mean, variance, Kurtosis and Skew
WYmean = WY['returns'].mean()
WYvar = WY['returns'].var()
WYkurt = WY['returns'].kurt()
WYskew = WY['returns'].skew()

#period before crash
crashstart = datetime.datetime(2020,2,20)
crashend = datetime.datetime(2020,3,23)
WYbef = web.DataReader('WY', 'yahoo', start, crashstart)
WYbef['avgreturnbefore'] = WYbef['Close'].pct_change(1)
WYbef['Cumulative Return Before Crash'] = (1 + WYbef['avgreturnbefore']).cumprod()

#period of crash
WYdur = web.DataReader('WY', 'yahoo', crashstart, crashend)
WYdur['avgreturnduring'] = WYdur['Close'].pct_change(1)
WYdur['Cumulative Return During Crash'] = (1 + WYdur['avgreturnduring']).cumprod()

#period after crash
WYaft = web.DataReader('WY', 'yahoo', crashend, end)
WYaft['avgreturnafter'] = WYaft['Close'].pct_change(1)
WYaft['Cumulative Return After Crash'] = (1 + WYaft['avgreturnafter']).cumprod()
In [685]:
#KDE Plot for Real Estate Sector
sp['returns'].plot(kind = 'kde', label ='S&P 500', alpha=0.5)
ARE['returns'].plot(kind = 'kde', label ='ARE', figsize = (10,8), alpha=0.5, title = 'KDE of Real Estate Stocks')
AMT['returns'].plot(kind = 'kde', label ='AMT', alpha=0.5)
AIV['returns'].plot(kind = 'kde', label ='AIV', alpha=0.5)
AVB['returns'].plot(kind = 'kde', label ='AVB', alpha=0.5)
BXP['returns'].plot(kind = 'kde', label ='BXP', alpha=0.5)
CBRE['returns'].plot(kind = 'kde', label ='CBRE', alpha=0.5)
CCI['returns'].plot(kind = 'kde', label ='CCI', alpha=0.5)
DLR['returns'].plot(kind = 'kde', label ='DLR', alpha=0.5)
DRE['returns'].plot(kind = 'kde', label ='DRE', alpha=0.5)
EQIX['returns'].plot(kind = 'kde', label ='EQIX', alpha=0.5)
EQR['returns'].plot(kind = 'kde', label ='EQR', alpha=0.5)
ESS['returns'].plot(kind = 'kde', label ='ESS', alpha=0.5)
EXR['returns'].plot(kind = 'kde', label ='EXR', alpha=0.5)
FRT['returns'].plot(kind = 'kde', label ='FRT', alpha=0.5)
PEAK['returns'].plot(kind = 'kde', label = 'PEAK', alpha=0.5)
HST['returns'].plot(kind = 'kde', label ='HST', alpha=0.5)
IRM['returns'].plot(kind = 'kde', label ='IRM', alpha=0.5)
KIM['returns'].plot(kind = 'kde', label ='KIM', alpha=0.5)
MAA['returns'].plot(kind = 'kde', label ='MAA', alpha=0.5)
PLD['returns'].plot(kind = 'kde', label ='PLD', alpha=0.5)
PSA['returns'].plot(kind = 'kde', label ='PSA', alpha=0.5)
O['returns'].plot(kind = 'kde', label ='O', alpha=0.5)
REG['returns'].plot(kind = 'kde', label ='REG', alpha=0.5)
SBAC['returns'].plot(kind = 'kde', label ='SBAC', alpha=0.5)
SPG['returns'].plot(kind = 'kde', label ='SPG', alpha=0.5)
SLG['returns'].plot(kind = 'kde', label ='SLG', alpha=0.5)
UDR['returns'].plot(kind = 'kde', label ='UDR', alpha=0.5)
VTR['returns'].plot(kind = 'kde', label ='VTR', alpha=0.5)
VNO['returns'].plot(kind = 'kde', label ='VNO', alpha=0.5)
WELL['returns'].plot(kind = 'kde', label ='WELL', alpha=0.5)
WY['returns'].plot(kind = 'kde', label ='WY', alpha=0.5)

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [686]:
#Boxplots comparing returns for Real Estate Sector
box_df = pd.concat([sp['returns'],ARE['returns'],AMT['returns'],AIV['returns'],AVB['returns'],BXP['returns'],CBRE['returns'],CCI['returns'],DLR['returns'],DRE['returns'],EQIX['returns'],EQR['returns'],ESS['returns'],EXR['returns'],FRT['returns'],PEAK['returns'],HST['returns'],IRM['returns'],KIM['returns'],MAA['returns'],PLD['returns'],PSA['returns'],O['returns'],REG['returns'],SBAC['returns'],SPG['returns'],SLG['returns'],UDR['returns'],VTR['returns'],VNO['returns'],WELL['returns'],WY['returns']], axis=1)
box_df.columns = ['S&P 500','ARE','AMT','AIV','AVB','BXP','CBRE','CCI','DLR','DRE','EQIX','EQR','ESS','EXR','FRT','PEAK','HST','IRM','KIM','MAA','PLD','PSA','O','REG','SBAC','SPG','SLG','UDR','VTR','VNO','WELL','WY']
box_df.plot(kind='box', figsize=(30,10), colormap= 'jet', title = 'Boxplots of Real Estate Sector Returns')
Out[686]:
<matplotlib.axes._subplots.AxesSubplot at 0x13fa2fa7e80>
In [687]:
#Cumulative Returns of Real Estate Stocks for Whole period

sp['Cumulative Return'].plot(label="S&P 500")
ARE['Cumulative Return'].plot(label='ARE', figsize=(16,8), title = 'Cumulative Return of Real Estate Stocks')
AMT['Cumulative Return'].plot(label="AMT")
AIV['Cumulative Return'].plot(label="AIV")
AVB['Cumulative Return'].plot(label="AVB")
BXP['Cumulative Return'].plot(label="BXP")
CBRE['Cumulative Return'].plot(label="CBRE")
CCI['Cumulative Return'].plot(label="CCI")
DLR['Cumulative Return'].plot(label="DLR")
DRE['Cumulative Return'].plot(label="DRE")
EQIX['Cumulative Return'].plot(label="EQIX")
EQR['Cumulative Return'].plot(label="EQR")
ESS['Cumulative Return'].plot(label="ESS")
EXR['Cumulative Return'].plot(label="EXR")
FRT['Cumulative Return'].plot(label="FRT")
PEAK['Cumulative Return'].plot(label="PEAK")
HST['Cumulative Return'].plot(label="HST")
IRM['Cumulative Return'].plot(label="IRM")
KIM['Cumulative Return'].plot(label="KIM")
MAA['Cumulative Return'].plot(label="MAA")
PLD['Cumulative Return'].plot(label="PLD")
PSA['Cumulative Return'].plot(label="PSA")
O['Cumulative Return'].plot(label="O")
REG['Cumulative Return'].plot(label="REG")
SBAC['Cumulative Return'].plot(label="SBAC")
SPG['Cumulative Return'].plot(label="SPG")
SLG['Cumulative Return'].plot(label="SLG")
UDR['Cumulative Return'].plot(label="UDR")
VTR['Cumulative Return'].plot(label="VTR")
VNO['Cumulative Return'].plot(label="VNO")
WELL['Cumulative Return'].plot(label="WELL")
WY['Cumulative Return'].plot(label="WY")

plt.legend(bbox_to_anchor=(1.2,1), loc="upper right");
In [688]:
#Sorting by Cumulative Return

cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],ARE['Cumulative Return'].iloc[-1],AMT['Cumulative Return'].iloc[-1],AIV['Cumulative Return'].iloc[-1],AVB['Cumulative Return'].iloc[-1],BXP['Cumulative Return'].iloc[-1],CBRE['Cumulative Return'].iloc[-1],CCI['Cumulative Return'].iloc[-1],DLR['Cumulative Return'].iloc[-1],DRE['Cumulative Return'].iloc[-1],EQIX['Cumulative Return'].iloc[-1],EQR['Cumulative Return'].iloc[-1],ESS['Cumulative Return'].iloc[-1],EXR['Cumulative Return'].iloc[-1],FRT['Cumulative Return'].iloc[-1],PEAK['Cumulative Return'].iloc[-1],HST['Cumulative Return'].iloc[-1],IRM['Cumulative Return'].iloc[-1],KIM['Cumulative Return'].iloc[-1],MAA['Cumulative Return'].iloc[-1],PLD['Cumulative Return'].iloc[-1],PSA['Cumulative Return'].iloc[-1],O['Cumulative Return'].iloc[-1],REG['Cumulative Return'].iloc[-1],SBAC['Cumulative Return'].iloc[-1],SPG['Cumulative Return'].iloc[-1],SLG['Cumulative Return'].iloc[-1],UDR['Cumulative Return'].iloc[-1],VTR['Cumulative Return'].iloc[-1],VNO['Cumulative Return'].iloc[-1],WELL['Cumulative Return'].iloc[-1],WY['Cumulative Return'].iloc[-1]],
            'Cumulative Returns 12/4-2/20': [spbef['Cumulative Return Before Crash'].iloc[-1],AREbef['Cumulative Return Before Crash'].iloc[-1],AMTbef['Cumulative Return Before Crash'].iloc[-1],AIVbef['Cumulative Return Before Crash'].iloc[-1],AVBbef['Cumulative Return Before Crash'].iloc[-1],BXPbef['Cumulative Return Before Crash'].iloc[-1],CBREbef['Cumulative Return Before Crash'].iloc[-1],CCIbef['Cumulative Return Before Crash'].iloc[-1],DLRbef['Cumulative Return Before Crash'].iloc[-1],DREbef['Cumulative Return Before Crash'].iloc[-1],EQIXbef['Cumulative Return Before Crash'].iloc[-1],EQRbef['Cumulative Return Before Crash'].iloc[-1],ESSbef['Cumulative Return Before Crash'].iloc[-1],EXRbef['Cumulative Return Before Crash'].iloc[-1],FRTbef['Cumulative Return Before Crash'].iloc[-1],PEAKbef['Cumulative Return Before Crash'].iloc[-1],HSTbef['Cumulative Return Before Crash'].iloc[-1],IRMbef['Cumulative Return Before Crash'].iloc[-1],KIMbef['Cumulative Return Before Crash'].iloc[-1],MAAbef['Cumulative Return Before Crash'].iloc[-1],PLDbef['Cumulative Return Before Crash'].iloc[-1],PSAbef['Cumulative Return Before Crash'].iloc[-1],Obef['Cumulative Return Before Crash'].iloc[-1],REGbef['Cumulative Return Before Crash'].iloc[-1],SBACbef['Cumulative Return Before Crash'].iloc[-1],SPGbef['Cumulative Return Before Crash'].iloc[-1],SLGbef['Cumulative Return Before Crash'].iloc[-1],UDRbef['Cumulative Return Before Crash'].iloc[-1],VTRbef['Cumulative Return Before Crash'].iloc[-1],VNObef['Cumulative Return Before Crash'].iloc[-1],WELLbef['Cumulative Return Before Crash'].iloc[-1],WYbef['Cumulative Return Before Crash'].iloc[-1]],
            'Cumulative Returns 2/20-3/23': [spdur['Cumulative Return During Crash'].iloc[-1],AREdur['Cumulative Return During Crash'].iloc[-1],AMTdur['Cumulative Return During Crash'].iloc[-1],AIVdur['Cumulative Return During Crash'].iloc[-1],AVBdur['Cumulative Return During Crash'].iloc[-1],BXPdur['Cumulative Return During Crash'].iloc[-1],CBREdur['Cumulative Return During Crash'].iloc[-1],CCIdur['Cumulative Return During Crash'].iloc[-1],DLRdur['Cumulative Return During Crash'].iloc[-1],DREdur['Cumulative Return During Crash'].iloc[-1],EQIXdur['Cumulative Return During Crash'].iloc[-1],EQRdur['Cumulative Return During Crash'].iloc[-1],ESSdur['Cumulative Return During Crash'].iloc[-1],EXRdur['Cumulative Return During Crash'].iloc[-1],FRTdur['Cumulative Return During Crash'].iloc[-1],PEAKdur['Cumulative Return During Crash'].iloc[-1],HSTdur['Cumulative Return During Crash'].iloc[-1],IRMdur['Cumulative Return During Crash'].iloc[-1],KIMdur['Cumulative Return During Crash'].iloc[-1],MAAdur['Cumulative Return During Crash'].iloc[-1],PLDdur['Cumulative Return During Crash'].iloc[-1],PSAdur['Cumulative Return During Crash'].iloc[-1],Odur['Cumulative Return During Crash'].iloc[-1],REGdur['Cumulative Return During Crash'].iloc[-1],SBACdur['Cumulative Return During Crash'].iloc[-1],SPGdur['Cumulative Return During Crash'].iloc[-1],SLGdur['Cumulative Return During Crash'].iloc[-1],UDRdur['Cumulative Return During Crash'].iloc[-1],VTRdur['Cumulative Return During Crash'].iloc[-1],VNOdur['Cumulative Return During Crash'].iloc[-1],WELLdur['Cumulative Return During Crash'].iloc[-1],WYdur['Cumulative Return During Crash'].iloc[-1]],
            'Cumulative Returns 3/23-6/5': [spaft['Cumulative Return After Crash'].iloc[-1],AREaft['Cumulative Return After Crash'].iloc[-1],AMTaft['Cumulative Return After Crash'].iloc[-1],AIVaft['Cumulative Return After Crash'].iloc[-1],AVBaft['Cumulative Return After Crash'].iloc[-1],BXPaft['Cumulative Return After Crash'].iloc[-1],CBREaft['Cumulative Return After Crash'].iloc[-1],CCIaft['Cumulative Return After Crash'].iloc[-1],DLRaft['Cumulative Return After Crash'].iloc[-1],DREaft['Cumulative Return After Crash'].iloc[-1],EQIXaft['Cumulative Return After Crash'].iloc[-1],EQRaft['Cumulative Return After Crash'].iloc[-1],ESSaft['Cumulative Return After Crash'].iloc[-1],EXRaft['Cumulative Return After Crash'].iloc[-1],FRTaft['Cumulative Return After Crash'].iloc[-1],PEAKaft['Cumulative Return After Crash'].iloc[-1],HSTaft['Cumulative Return After Crash'].iloc[-1],IRMaft['Cumulative Return After Crash'].iloc[-1],KIMaft['Cumulative Return After Crash'].iloc[-1],MAAaft['Cumulative Return After Crash'].iloc[-1],PLDaft['Cumulative Return After Crash'].iloc[-1],PSAaft['Cumulative Return After Crash'].iloc[-1],Oaft['Cumulative Return After Crash'].iloc[-1],REGaft['Cumulative Return After Crash'].iloc[-1],SBACaft['Cumulative Return After Crash'].iloc[-1],SPGaft['Cumulative Return After Crash'].iloc[-1],SLGaft['Cumulative Return After Crash'].iloc[-1],UDRaft['Cumulative Return After Crash'].iloc[-1],VTRaft['Cumulative Return After Crash'].iloc[-1],VNOaft['Cumulative Return After Crash'].iloc[-1],WELLaft['Cumulative Return After Crash'].iloc[-1],WYaft['Cumulative Return After Crash'].iloc[-1]]}                         

redf2 = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','ARE','AMT','AIV','AVB','BXP','CBRE','CCI','DLR','DRE','EQIX','EQR','ESS','EXR','FRT','PEAK','HST','IRM','KIM','MAA','PLD','PSA','O','REG','SBAC','SPG','SLG','UDR','VTR','VNO','WELL','WY'])

pd.DataFrame(redf2)
Out[688]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
ARE 0.990754 1.054039 0.681128 1.380003
AMT 1.241517 1.160199 0.723479 1.479089
AIV 0.799280 1.044499 0.458484 1.669039
AVB 0.802016 1.054941 0.533040 1.426247
BXP 0.739420 1.064638 0.530425 1.309380
CBRE 0.947221 1.116956 0.468289 1.810929
CCI 1.253252 1.241285 0.700521 1.441272
DLR 1.186230 1.144416 0.791636 1.309360
DRE 1.049615 1.102652 0.678821 1.402286
EQIX 1.196419 1.151813 0.754392 1.376906
EQR 0.788131 1.025519 0.594792 1.292080
ESS 0.842950 1.047137 0.558799 1.440597
EXR 0.970784 1.008079 0.695275 1.385069
FRT 0.763345 0.974800 0.522287 1.499325
PEAK 0.853446 1.084327 0.534996 1.471178
HST 0.798291 0.978917 0.621653 1.311798
IRM 0.912750 1.054685 0.652490 1.326339
KIM 0.715562 0.891396 0.496308 1.617428
MAA 0.923488 1.096104 0.573257 1.469706
PLD 1.050604 1.086169 0.640424 1.510336
PSA 0.969101 1.044283 0.722232 1.284914
O 0.819199 1.078615 0.529117 1.435395
REG 0.786743 0.992102 0.544490 1.456422
SBAC 1.262661 1.241205 0.737548 1.379281
SPG 0.594073 0.942269 0.356934 1.766348
SLG 0.641268 1.097052 0.436916 1.337871
UDR 0.878265 1.068450 0.592155 1.388149
VTR 0.745649 1.075306 0.368590 1.881304
VNO 0.677924 0.995817 0.476198 1.429598
WELL 0.735259 1.050609 0.494092 1.416417
WY 0.791216 1.042905 0.436346 1.738679
In [709]:
redf2.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[709]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
VTR 0.745649 1.075306 0.368590 1.881304
CBRE 0.947221 1.116956 0.468289 1.810929
SPG 0.594073 0.942269 0.356934 1.766348
WY 0.791216 1.042905 0.436346 1.738679
AIV 0.799280 1.044499 0.458484 1.669039
KIM 0.715562 0.891396 0.496308 1.617428
PLD 1.050604 1.086169 0.640424 1.510336
FRT 0.763345 0.974800 0.522287 1.499325
AMT 1.241517 1.160199 0.723479 1.479089
PEAK 0.853446 1.084327 0.534996 1.471178
MAA 0.923488 1.096104 0.573257 1.469706
REG 0.786743 0.992102 0.544490 1.456422
CCI 1.253252 1.241285 0.700521 1.441272
ESS 0.842950 1.047137 0.558799 1.440597
O 0.819199 1.078615 0.529117 1.435395
VNO 0.677924 0.995817 0.476198 1.429598
S&P 500 1.026077 1.083678 0.663281 1.427519
AVB 0.802016 1.054941 0.533040 1.426247
WELL 0.735259 1.050609 0.494092 1.416417
DRE 1.049615 1.102652 0.678821 1.402286
UDR 0.878265 1.068450 0.592155 1.388149
EXR 0.970784 1.008079 0.695275 1.385069
ARE 0.990754 1.054039 0.681128 1.380003
SBAC 1.262661 1.241205 0.737548 1.379281
EQIX 1.196419 1.151813 0.754392 1.376906
SLG 0.641268 1.097052 0.436916 1.337871
IRM 0.912750 1.054685 0.652490 1.326339
HST 0.798291 0.978917 0.621653 1.311798
BXP 0.739420 1.064638 0.530425 1.309380
DLR 1.186230 1.144416 0.791636 1.309360
EQR 0.788131 1.025519 0.594792 1.292080
PSA 0.969101 1.044283 0.722232 1.284914
In [690]:
#Table of Key Statistics of Real Estate Stocks
import pandas as pd
import statistics as stats

remeans = [AREmean,AMTmean,AIVmean,AVBmean,BXPmean,CBREmean,CCImean,DLRmean,DREmean,EQIXmean,EQRmean,ESSmean,EXRmean,FRTmean,PEAKmean,HSTmean,IRMmean,KIMmean,MAAmean,PLDmean,PSAmean,Omean,REGmean,SBACmean,SPGmean,SLGmean,UDRmean,VTRmean,VNOmean,WELLmean,WYmean]
reaveragemean = stats.mean(remeans)
revariances = [AREvar,AMTvar,AIVvar,AVBvar,BXPvar,CBREvar,CCIvar,DLRvar,DREvar,EQIXvar,EQRvar,ESSvar,EXRvar,FRTvar,PEAKvar,HSTvar,IRMvar,KIMvar,MAAvar,PLDvar,PSAvar,Ovar,REGvar,SBACvar,SPGvar,SLGvar,UDRvar,VTRvar,VNOvar,WELLvar,WYvar]
reaveragevar = stats.mean(revariances)
rekurtoses = [AREkurt,AMTkurt,AIVkurt,AVBkurt,BXPkurt,CBREkurt,CCIkurt,DLRkurt,DREkurt,EQIXkurt,EQRkurt,ESSkurt,EXRkurt,FRTkurt,PEAKkurt,HSTkurt,IRMkurt,KIMkurt,MAAkurt,PLDkurt,PSAkurt,Okurt,REGkurt,SBACkurt,SPGkurt,SLGkurt,UDRkurt,VTRkurt,VNOkurt,WELLkurt,WYkurt]
reaveragekurt = stats.mean(rekurtoses)
reskews = [AREskew,AMTskew,AIVskew,AVBskew,BXPskew,CBREskew,CCIskew,DLRskew,DREskew,EQIXskew,EQRskew,ESSskew,EXRskew,FRTskew,PEAKskew,HSTskew,IRMskew,KIMskew,MAAskew,PLDskew,PSAskew,Oskew,REGskew,SBACskew,SPGskew,SLGskew,UDRskew,VTRskew,VNOskew,WELLskew,WYskew]
reaverageskew = stats.mean(reskews)

Real_Estate = {'Mean': [[spmean],[reaveragemean],[AREmean], [AMTmean],[AIVmean],[AVBmean],[BXPmean],[CBREmean],[CCImean],[DLRmean],[DREmean],[EQIXmean],[EQRmean],[ESSmean],[EXRmean],[FRTmean],[PEAKmean],[HSTmean],[IRMmean],[KIMmean],[MAAmean],[PLDmean],[PSAmean],[Omean],[REGmean],[SBACmean],[SPGmean],[SLGmean],[UDRmean],[VTRmean],[VNOmean],[WELLmean],[WYmean]], 
        'Variance': [[spvar],[reaveragevar],[AREvar],[AMTvar],[AIVvar],[AVBvar],[BXPvar],[CBREvar],[CCIvar],[DLRvar],[DREvar],[EQIXvar],[EQRvar],[ESSvar],[EXRvar],[FRTvar],[PEAKvar],[HSTvar],[IRMvar],[KIMvar],[MAAvar],[PLDvar],[PSAvar],[Ovar],[REGvar],[SBACvar],[SPGvar],[SLGvar],[UDRvar],[VTRvar],[VNOvar],[WELLvar],[WYvar]], 
          'Kurtosis': [[spkurt],[reaveragekurt],[AREkurt],[AMTkurt],[AIVkurt],[AVBkurt],[BXPkurt],[CBREkurt],[CCIkurt],[DLRkurt],[DREkurt],[EQIXkurt],[EQRkurt],[ESSkurt],[EXRkurt],[FRTkurt],[PEAKkurt],[HSTkurt],[IRMkurt],[KIMkurt],[MAAkurt],[PLDkurt],[PSAkurt],[Okurt],[REGkurt],[SBACkurt],[SPGkurt],[SLGkurt],[UDRkurt],[VTRkurt],[VNOkurt],[WELLkurt],[WYkurt]],
          'Skew': [[spskew],[reaverageskew],[AREskew],[AMTskew],[AIVskew],[AVBskew],[BXPskew],[CBREskew],[CCIskew],[DLRskew],[DREskew],[EQIXskew],[EQRskew],[ESSskew],[EXRskew],[FRTskew],[PEAKskew],[HSTskew],[IRMskew],[KIMskew],[MAAskew],[PLDskew],[PSAskew],[Oskew],[REGskew],[SBACskew],[SPGskew],[SLGskew],[UDRskew],[VTRskew],[VNOskew],[WELLskew],[WYskew]],
        }

redf = pd.DataFrame(Real_Estate, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500','Average','ARE','AMT','AIV','AVB','BXP','CBRE','CCI','DLR','DRE','EQIX','EQR','ESS','EXR','FRT','PEAK','HST','IRM','KIM','MAA','PLD','PSA','O','REG','SBAC','SPG','SLG','UDR','VTR','VNO','WELL','WY']
)

pd.DataFrame(redf)
Out[690]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Average [-4.745029486981169e-05] [0.001957559239860217] [4.387800904739723] [-0.4307897146096574]
ARE [0.0005215484406331389] [0.0011804155545988668] [6.699799332405603] [-0.51424020182126]
AMT [0.0023458319617127394] [0.0012647573999279365] [3.926359306110725] [0.06758310888353604]
AIV [-0.0006930554723297368] [0.0021183882620816794] [7.026325464933939] [-0.650462770125205]
AVB [-0.0009639134674155821] [0.0015559619700361838] [3.6820061713913557] [-0.4596483592749585]
BXP [-0.0015295235490582688] [0.0016985885220052069] [3.943799603296158] [-0.6497203824253506]
CBRE [0.0006885040476309969] [0.002230267956530158] [2.432229052993688] [-0.20825751302718104]
CCI [0.0023801042834718632] [0.0011820959005912268] [2.4893359004247353] [-0.018746873692000548]
DLR [0.0019451258823801204] [0.0011917215189211406] [3.1716623420448027] [0.23063489234900977]
DRE [0.0011262604881769008] [0.0014850240654751745] [6.159914316980614] [-0.07391850605932179]
EQIX [0.0019266270068702574] [0.001016079235009097] [3.4764663136409926] [0.20759463927491303]
EQR [-0.0012013927029410735] [0.0013629520856755046] [3.66873205304267] [-0.40237010112463945]
ESS [-0.0006292890285166945] [0.0014197045670327664] [5.765568835666431] [-0.9545397520937061]
EXR [0.0002765602380334252] [0.0010076218651341802] [4.681951706483913] [-0.9885667705555953]
FRT [-0.001090298492574639] [0.002035018981977294] [6.1300120776196065] [-1.0141385048160485]
PEAK [-0.00014947819177854198] [0.0021684813463114555] [5.699401706230176] [-0.6510349703102037]
HST [-0.000771082086904448] [0.0020538356564154285] [2.8890892636470875] [0.315934735034454]
IRM [-8.441429542239645e-05] [0.0012720340508455097] [3.7205631179666607] [-0.42845977521658124]
KIM [-0.0012006647136508619] [0.002865198757140332] [1.8783095534573206] [-0.39709855469542865]
MAA [9.052134281063739e-05] [0.0014177200704022417] [4.8185786523477505] [-0.8679932765033831]
PLD [0.0010814479586494631] [0.0013680335690895607] [4.717458705534551] [-0.5036805941477672]
PSA [0.00015515153972509154] [0.0008000418366221047] [2.8963783758280615] [-0.8913157490209178]
O [-0.00035625719730567626] [0.0023232772837943907] [9.725982421680353] [-1.478307990990129]
REG [-0.0007611483151118509] [0.002237081986996497] [4.459889215898195] [-0.6045967309290438]
SBAC [0.002401540096388933] [0.001118833924645039] [1.7240438895314725] [0.49539698560038753]
SPG [-0.0017379278653373773] [0.004669261241043329] [3.6318027010752134] [-0.3015191339718835]
SLG [-0.0021793699859183513] [0.0026564505552594446] [2.277600259371268] [-0.25595677950790446]
UDR [-0.00031380981132790787] [0.001418047758953706] [4.101915973782774] [-0.5074829562744705]
VTR [5.1060928764330474e-05] [0.004552067224656544] [5.456318690458196] [-0.7405283620912859]
VNO [-0.0018423098531443203] [0.00239757898636679] [4.160433808066772] [-0.932890241398213]
WELL [-0.000742708769373491] [0.0033444957838951314] [4.919683081228488] [-0.22146832064928432]
WY [-0.00021459955810084207] [0.00327329851823281] [5.690216153791835] [0.04531765668008249]
In [691]:
redf.sort_values("Mean", ascending=False)
Out[691]:
Mean Variance Kurtosis Skew
SBAC [0.002401540096388933] [0.001118833924645039] [1.7240438895314725] [0.49539698560038753]
CCI [0.0023801042834718632] [0.0011820959005912268] [2.4893359004247353] [-0.018746873692000548]
AMT [0.0023458319617127394] [0.0012647573999279365] [3.926359306110725] [0.06758310888353604]
DLR [0.0019451258823801204] [0.0011917215189211406] [3.1716623420448027] [0.23063489234900977]
EQIX [0.0019266270068702574] [0.001016079235009097] [3.4764663136409926] [0.20759463927491303]
DRE [0.0011262604881769008] [0.0014850240654751745] [6.159914316980614] [-0.07391850605932179]
PLD [0.0010814479586494631] [0.0013680335690895607] [4.717458705534551] [-0.5036805941477672]
CBRE [0.0006885040476309969] [0.002230267956530158] [2.432229052993688] [-0.20825751302718104]
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
ARE [0.0005215484406331389] [0.0011804155545988668] [6.699799332405603] [-0.51424020182126]
EXR [0.0002765602380334252] [0.0010076218651341802] [4.681951706483913] [-0.9885667705555953]
PSA [0.00015515153972509154] [0.0008000418366221047] [2.8963783758280615] [-0.8913157490209178]
MAA [9.052134281063739e-05] [0.0014177200704022417] [4.8185786523477505] [-0.8679932765033831]
VTR [5.1060928764330474e-05] [0.004552067224656544] [5.456318690458196] [-0.7405283620912859]
Average [-4.745029486981169e-05] [0.001957559239860217] [4.387800904739723] [-0.4307897146096574]
IRM [-8.441429542239645e-05] [0.0012720340508455097] [3.7205631179666607] [-0.42845977521658124]
PEAK [-0.00014947819177854198] [0.0021684813463114555] [5.699401706230176] [-0.6510349703102037]
WY [-0.00021459955810084207] [0.00327329851823281] [5.690216153791835] [0.04531765668008249]
UDR [-0.00031380981132790787] [0.001418047758953706] [4.101915973782774] [-0.5074829562744705]
O [-0.00035625719730567626] [0.0023232772837943907] [9.725982421680353] [-1.478307990990129]
ESS [-0.0006292890285166945] [0.0014197045670327664] [5.765568835666431] [-0.9545397520937061]
AIV [-0.0006930554723297368] [0.0021183882620816794] [7.026325464933939] [-0.650462770125205]
WELL [-0.000742708769373491] [0.0033444957838951314] [4.919683081228488] [-0.22146832064928432]
REG [-0.0007611483151118509] [0.002237081986996497] [4.459889215898195] [-0.6045967309290438]
HST [-0.000771082086904448] [0.0020538356564154285] [2.8890892636470875] [0.315934735034454]
AVB [-0.0009639134674155821] [0.0015559619700361838] [3.6820061713913557] [-0.4596483592749585]
FRT [-0.001090298492574639] [0.002035018981977294] [6.1300120776196065] [-1.0141385048160485]
KIM [-0.0012006647136508619] [0.002865198757140332] [1.8783095534573206] [-0.39709855469542865]
EQR [-0.0012013927029410735] [0.0013629520856755046] [3.66873205304267] [-0.40237010112463945]
BXP [-0.0015295235490582688] [0.0016985885220052069] [3.943799603296158] [-0.6497203824253506]
SPG [-0.0017379278653373773] [0.004669261241043329] [3.6318027010752134] [-0.3015191339718835]
VNO [-0.0018423098531443203] [0.00239757898636679] [4.160433808066772] [-0.932890241398213]
SLG [-0.0021793699859183513] [0.0026564505552594446] [2.277600259371268] [-0.25595677950790446]
In [692]:
#Average Stats across Sectors
Averages = {'Mean': [[spmean],[averagemean],[mataveragemean],[indaveragemean],[cdaveragemean],[cosaveragemean],[haveragemean],[faveragemean],[itaveragemean],[csaveragemean],[uaveragemean],[reaveragemean]],
            'Variance': [[spvar],[averagevar],[mataveragevar],[indaveragevar],[cdaveragevar],[cosaveragevar],[haveragevar],[faveragevar],[itaveragevar],[csaveragevar],[uaveragevar],[reaveragevar]],
            'Kurtosis': [[spkurt],[averagekurt],[mataveragekurt],[indaveragekurt],[cdaveragekurt],[cosaveragekurt],[haveragekurt],[faveragekurt],[itaveragekurt],[csaveragekurt],[uaveragekurt],[reaveragekurt]],
            'Skew': [[spskew],[averageskew],[mataverageskew],[indaverageskew],[cdaverageskew],[cosaverageskew],[haverageskew],[faverageskew],[itaverageskew],[csaverageskew],[uaverageskew],[reaverageskew]]
           }

avdf = pd.DataFrame(Averages, columns = ['Mean', 'Variance', 'Kurtosis', 'Skew'], index= ['S&P 500', 'Energy','Materials','Industrials','Consumer Discretionary','Consumer Staples','Healthcare','Financials','Information Technology','Communication Services','Utilities','Real Estate'])

pd.DataFrame(avdf)
Out[692]:
Mean Variance Kurtosis Skew
S&P 500 [0.0005995311129481497] [0.0007895307574545688] [4.755946478632353] [-0.3738492604880212]
Energy [-0.000269796662685561] [0.003880462737369595] [6.9688586637195975] [-0.5619505120891912]
Materials [0.0006024611705163008] [0.0017262583228038184] [4.736414372650555] [0.03830609533180734]
Industrials [0.000426072351837813] [0.001699801551972132] [4.9568321834711035] [-0.024413233149524257]
Consumer Discretionary [0.00023121301811639431] [0.002510920851468141] [5.574391146207624] [0.09733454086984512]
Consumer Staples [0.00019676499906751805] [0.0010751314445893423] [5.54886888026982] [0.2680625577374681]
Healthcare [0.001012868830071905] [0.0011254848167398985] [3.6670562450420836] [0.07386041378796937]
Financials [2.515872452493875e-05] [0.0020734337246651737] [4.282598466014067] [-0.00011166298689266822]
Information Technology [0.001223507760026338] [0.0015945420321274149] [4.9155529057131195] [-0.00025923980184740865]
Communication Services [0.0003912645853900008] [0.0013372232036933404] [3.1238021784062884] [0.0038761459127950137]
Utilities [0.00016596619104204964] [0.0013407448831021097] [5.209679271543116] [-0.0035316127438560527]
Real Estate [-4.745029486981169e-05] [0.001957559239860217] [4.387800904739723] [-0.4307897146096574]
In [693]:
#Cumulative Returns ALL STOCKS
cumret = {'Cumulative Returns 12/4-6/5': [sp['Cumulative Return'].iloc[-1],apache['Cumulative Return'].iloc[-1],baker['Cumulative Return'].iloc[-1],cabot['Cumulative Return'].iloc[-1],chevron['Cumulative Return'].iloc[-1],concho['Cumulative Return'].iloc[-1],conoco['Cumulative Return'].iloc[-1],devon['Cumulative Return'].iloc[-1],diamond['Cumulative Return'].iloc[-1],EOG['Cumulative Return'].iloc[-1],exxon['Cumulative Return'].iloc[-1],halliburton['Cumulative Return'].iloc[-1],hess['Cumulative Return'].iloc[-1],holly['Cumulative Return'].iloc[-1],kinder['Cumulative Return'].iloc[-1],marathonoil['Cumulative Return'].iloc[-1],marathonpet['Cumulative Return'].iloc[-1],national['Cumulative Return'].iloc[-1],noble['Cumulative Return'].iloc[-1],occidental['Cumulative Return'].iloc[-1],oneok['Cumulative Return'].iloc[-1],phillips66['Cumulative Return'].iloc[-1],pioneer['Cumulative Return'].iloc[-1],schlumberger['Cumulative Return'].iloc[-1],technip['Cumulative Return'].iloc[-1],valero['Cumulative Return'].iloc[-1],williams['Cumulative Return'].iloc[-1],APD['Cumulative Return'].iloc[-1],ALB['Cumulative Return'].iloc[-1],AMCR['Cumulative Return'].iloc[-1],AVY['Cumulative Return'].iloc[-1],BLL['Cumulative Return'].iloc[-1],CE['Cumulative Return'].iloc[-1],CF['Cumulative Return'].iloc[-1],CTVA['Cumulative Return'].iloc[-1],DOW['Cumulative Return'].iloc[-1],DD['Cumulative Return'].iloc[-1],EMN['Cumulative Return'].iloc[-1],ECL['Cumulative Return'].iloc[-1],FMC['Cumulative Return'].iloc[-1],FCX['Cumulative Return'].iloc[-1],IP['Cumulative Return'].iloc[-1],IFF['Cumulative Return'].iloc[-1],LIN['Cumulative Return'].iloc[-1],LYB['Cumulative Return'].iloc[-1],MLM['Cumulative Return'].iloc[-1],MOS['Cumulative Return'].iloc[-1],NEM['Cumulative Return'].iloc[-1],NUE['Cumulative Return'].iloc[-1],PKG['Cumulative Return'].iloc[-1],PPG['Cumulative Return'].iloc[-1],SEE['Cumulative Return'].iloc[-1],SHW['Cumulative Return'].iloc[-1],VMC['Cumulative Return'].iloc[-1],WRK['Cumulative Return'].iloc[-1],MMM['Cumulative Return'].iloc[-1],ALK['Cumulative Return'].iloc[-1],ALLE['Cumulative Return'].iloc[-1],AAL['Cumulative Return'].iloc[-1],AME['Cumulative Return'].iloc[-1],AOS['Cumulative Return'].iloc[-1],BA['Cumulative Return'].iloc[-1],CHRW['Cumulative Return'].iloc[-1],CAT['Cumulative Return'].iloc[-1],CTAS['Cumulative Return'].iloc[-1],CPRT['Cumulative Return'].iloc[-1],CSX['Cumulative Return'].iloc[-1],CMI['Cumulative Return'].iloc[-1],DE['Cumulative Return'].iloc[-1],DAL['Cumulative Return'].iloc[-1],DOV['Cumulative Return'].iloc[-1],ETN['Cumulative Return'].iloc[-1],EMR['Cumulative Return'].iloc[-1],EFX['Cumulative Return'].iloc[-1],EXPD['Cumulative Return'].iloc[-1],FAST['Cumulative Return'].iloc[-1],FDX['Cumulative Return'].iloc[-1],FLS['Cumulative Return'].iloc[-1],FTV['Cumulative Return'].iloc[-1],FBHS['Cumulative Return'].iloc[-1],GD['Cumulative Return'].iloc[-1],GE['Cumulative Return'].iloc[-1],GWW['Cumulative Return'].iloc[-1],HON['Cumulative Return'].iloc[-1],HWM['Cumulative Return'].iloc[-1],HII['Cumulative Return'].iloc[-1],IEX['Cumulative Return'].iloc[-1],INFO['Cumulative Return'].iloc[-1],ITW['Cumulative Return'].iloc[-1],IR['Cumulative Return'].iloc[-1],J['Cumulative Return'].iloc[-1],JBHT['Cumulative Return'].iloc[-1],JCI['Cumulative Return'].iloc[-1],KSU['Cumulative Return'].iloc[-1],LHX['Cumulative Return'].iloc[-1],LMT['Cumulative Return'].iloc[-1],MAS['Cumulative Return'].iloc[-1],NLSN['Cumulative Return'].iloc[-1],NSC['Cumulative Return'].iloc[-1],NOC['Cumulative Return'].iloc[-1],ODFL['Cumulative Return'].iloc[-1],PCAR['Cumulative Return'].iloc[-1],PH['Cumulative Return'].iloc[-1],PNR['Cumulative Return'].iloc[-1],PWR['Cumulative Return'].iloc[-1],RTX['Cumulative Return'].iloc[-1],RSG['Cumulative Return'].iloc[-1],RHI['Cumulative Return'].iloc[-1],ROK['Cumulative Return'].iloc[-1],ROL['Cumulative Return'].iloc[-1],ROP['Cumulative Return'].iloc[-1],SNA['Cumulative Return'].iloc[-1],LUV['Cumulative Return'].iloc[-1],SWK['Cumulative Return'].iloc[-1],TXT['Cumulative Return'].iloc[-1],TT['Cumulative Return'].iloc[-1],TDG['Cumulative Return'].iloc[-1],UNP['Cumulative Return'].iloc[-1],UAL['Cumulative Return'].iloc[-1],UPS['Cumulative Return'].iloc[-1],URI['Cumulative Return'].iloc[-1],VRSK['Cumulative Return'].iloc[-1],WAB['Cumulative Return'].iloc[-1],WM['Cumulative Return'].iloc[-1],XYL['Cumulative Return'].iloc[-1],AAP['Cumulative Return'].iloc[-1],AMZN['Cumulative Return'].iloc[-1],APTV['Cumulative Return'].iloc[-1],AZO['Cumulative Return'].iloc[-1],BBY['Cumulative Return'].iloc[-1],BKNG['Cumulative Return'].iloc[-1],BWA['Cumulative Return'].iloc[-1],KMX['Cumulative Return'].iloc[-1],CCL['Cumulative Return'].iloc[-1],CMG['Cumulative Return'].iloc[-1],DHI['Cumulative Return'].iloc[-1],DRI['Cumulative Return'].iloc[-1],DG['Cumulative Return'].iloc[-1],DLTR['Cumulative Return'].iloc[-1],DPZ['Cumulative Return'].iloc[-1],EBAY['Cumulative 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Crash'].iloc[-1],SIVBaft['Cumulative Return After Crash'].iloc[-1],SYFaft['Cumulative Return After Crash'].iloc[-1],TROWaft['Cumulative Return After Crash'].iloc[-1],TRVaft['Cumulative Return After Crash'].iloc[-1],TFCaft['Cumulative Return After Crash'].iloc[-1],USBaft['Cumulative Return After Crash'].iloc[-1],UNMaft['Cumulative Return After Crash'].iloc[-1],WRBaft['Cumulative Return After Crash'].iloc[-1],WFCaft['Cumulative Return After Crash'].iloc[-1],WLTWaft['Cumulative Return After Crash'].iloc[-1],ZIONaft['Cumulative Return After Crash'].iloc[-1],ACNaft['Cumulative Return After Crash'].iloc[-1],ADBEaft['Cumulative Return After Crash'].iloc[-1],AMDaft['Cumulative Return After Crash'].iloc[-1],AKAMaft['Cumulative Return After Crash'].iloc[-1],ADSaft['Cumulative Return After Crash'].iloc[-1],APHaft['Cumulative Return After Crash'].iloc[-1],ADIaft['Cumulative Return After Crash'].iloc[-1],ANSSaft['Cumulative Return After Crash'].iloc[-1],AAPLaft['Cumulative Return After Crash'].iloc[-1],AMATaft['Cumulative Return After Crash'].iloc[-1],ANETaft['Cumulative Return After Crash'].iloc[-1],ADSKaft['Cumulative Return After Crash'].iloc[-1],ADPaft['Cumulative Return After Crash'].iloc[-1],AVGOaft['Cumulative Return After Crash'].iloc[-1],BRaft['Cumulative Return After Crash'].iloc[-1],CDNSaft['Cumulative Return After Crash'].iloc[-1],CDWaft['Cumulative Return After Crash'].iloc[-1],CSCOaft['Cumulative Return After Crash'].iloc[-1],CTXSaft['Cumulative Return After Crash'].iloc[-1],CTSHaft['Cumulative Return After Crash'].iloc[-1],GLWaft['Cumulative Return After Crash'].iloc[-1],DXCaft['Cumulative Return After Crash'].iloc[-1],FFIVaft['Cumulative Return After Crash'].iloc[-1],FISaft['Cumulative Return After Crash'].iloc[-1],FISVaft['Cumulative Return After Crash'].iloc[-1],FLTaft['Cumulative Return After Crash'].iloc[-1],FLIRaft['Cumulative Return After Crash'].iloc[-1],FTNTaft['Cumulative Return After Crash'].iloc[-1],ITaft['Cumulative Return After Crash'].iloc[-1],GPNaft['Cumulative Return After Crash'].iloc[-1],HPEaft['Cumulative Return After Crash'].iloc[-1],HPQaft['Cumulative Return After Crash'].iloc[-1],INTCaft['Cumulative Return After Crash'].iloc[-1],IBMaft['Cumulative Return After Crash'].iloc[-1],INTUaft['Cumulative Return After Crash'].iloc[-1],IPGPaft['Cumulative Return After Crash'].iloc[-1],JKHYaft['Cumulative Return After Crash'].iloc[-1],JNPRaft['Cumulative Return After Crash'].iloc[-1],KEYSaft['Cumulative Return After Crash'].iloc[-1],KLACaft['Cumulative Return After Crash'].iloc[-1],LRCXaft['Cumulative Return After Crash'].iloc[-1],LDOSaft['Cumulative Return After Crash'].iloc[-1],MAaft['Cumulative Return After Crash'].iloc[-1],MXIMaft['Cumulative Return After Crash'].iloc[-1],MCHPaft['Cumulative Return After Crash'].iloc[-1],MUaft['Cumulative Return After Crash'].iloc[-1],MSFTaft['Cumulative Return After Crash'].iloc[-1],MSIaft['Cumulative Return After Crash'].iloc[-1],NTAPaft['Cumulative Return After Crash'].iloc[-1],NLOKaft['Cumulative Return After Crash'].iloc[-1],NVDAaft['Cumulative Return After Crash'].iloc[-1],ORCLaft['Cumulative Return After Crash'].iloc[-1],PAYXaft['Cumulative Return After Crash'].iloc[-1],PAYCaft['Cumulative Return After Crash'].iloc[-1],PYPLaft['Cumulative Return After Crash'].iloc[-1],QRVOaft['Cumulative Return After Crash'].iloc[-1],QCOMaft['Cumulative Return After Crash'].iloc[-1],CRMaft['Cumulative Return After Crash'].iloc[-1],STXaft['Cumulative Return After Crash'].iloc[-1],NOWaft['Cumulative Return After Crash'].iloc[-1],SWKSaft['Cumulative Return After Crash'].iloc[-1],SNPSaft['Cumulative Return After Crash'].iloc[-1],TELaft['Cumulative Return After Crash'].iloc[-1],TXNaft['Cumulative Return After Crash'].iloc[-1],VRSNaft['Cumulative Return After Crash'].iloc[-1],Vaft['Cumulative Return After Crash'].iloc[-1],WDCaft['Cumulative Return After Crash'].iloc[-1],WUaft['Cumulative Return After Crash'].iloc[-1],XRXaft['Cumulative Return After Crash'].iloc[-1],XLNXaft['Cumulative Return After Crash'].iloc[-1],ZBRAaft['Cumulative Return After Crash'].iloc[-1],ATVIaft['Cumulative Return After Crash'].iloc[-1],GOOGLaft['Cumulative Return After Crash'].iloc[-1],GOOGaft['Cumulative Return After Crash'].iloc[-1],Taft['Cumulative Return After Crash'].iloc[-1],CTLaft['Cumulative Return After Crash'].iloc[-1],CHTRaft['Cumulative Return After Crash'].iloc[-1],CMCSAaft['Cumulative Return After Crash'].iloc[-1],DISCAaft['Cumulative Return After Crash'].iloc[-1],DISCKaft['Cumulative Return After Crash'].iloc[-1],DISHaft['Cumulative Return After Crash'].iloc[-1],EAaft['Cumulative Return After Crash'].iloc[-1],FBaft['Cumulative Return After Crash'].iloc[-1],FOXAaft['Cumulative Return After Crash'].iloc[-1],FOXaft['Cumulative Return After Crash'].iloc[-1],IPGaft['Cumulative Return After Crash'].iloc[-1],LYVaft['Cumulative Return After Crash'].iloc[-1],NFLXaft['Cumulative Return After Crash'].iloc[-1],NWSAaft['Cumulative Return After Crash'].iloc[-1],NWSaft['Cumulative Return After Crash'].iloc[-1],OMCaft['Cumulative Return After Crash'].iloc[-1],TMUSaft['Cumulative Return After Crash'].iloc[-1],TTWOaft['Cumulative Return After Crash'].iloc[-1],TWTRaft['Cumulative Return After Crash'].iloc[-1],VZaft['Cumulative Return After Crash'].iloc[-1],VIACaft['Cumulative Return After Crash'].iloc[-1],DISaft['Cumulative Return After Crash'].iloc[-1],AESaft['Cumulative Return After Crash'].iloc[-1],LNTaft['Cumulative Return After Crash'].iloc[-1],AEEaft['Cumulative Return After Crash'].iloc[-1],AEPaft['Cumulative Return After Crash'].iloc[-1],AWKaft['Cumulative Return After Crash'].iloc[-1],ATOaft['Cumulative Return After Crash'].iloc[-1],CNPaft['Cumulative Return After Crash'].iloc[-1],CMSaft['Cumulative Return After Crash'].iloc[-1],EDaft['Cumulative Return After Crash'].iloc[-1],Daft['Cumulative Return After Crash'].iloc[-1],DTEaft['Cumulative Return After Crash'].iloc[-1],DUKaft['Cumulative Return After Crash'].iloc[-1],EIXaft['Cumulative Return After Crash'].iloc[-1],ETRaft['Cumulative Return After Crash'].iloc[-1],EVRGaft['Cumulative Return After Crash'].iloc[-1],ESaft['Cumulative Return After Crash'].iloc[-1],EXCaft['Cumulative Return After Crash'].iloc[-1],FEaft['Cumulative Return After Crash'].iloc[-1],NEEaft['Cumulative Return After Crash'].iloc[-1],NIaft['Cumulative Return After Crash'].iloc[-1],NRGaft['Cumulative Return After Crash'].iloc[-1],PNWaft['Cumulative Return After Crash'].iloc[-1],PPLaft['Cumulative Return After Crash'].iloc[-1],PEGaft['Cumulative Return After Crash'].iloc[-1],SREaft['Cumulative Return After Crash'].iloc[-1],SOaft['Cumulative Return After Crash'].iloc[-1],WECaft['Cumulative Return After Crash'].iloc[-1],XELaft['Cumulative Return After Crash'].iloc[-1],AREaft['Cumulative Return After Crash'].iloc[-1],AMTaft['Cumulative Return After Crash'].iloc[-1],AIVaft['Cumulative Return After Crash'].iloc[-1],AVBaft['Cumulative Return After Crash'].iloc[-1],BXPaft['Cumulative Return After Crash'].iloc[-1],CBREaft['Cumulative Return After Crash'].iloc[-1],CCIaft['Cumulative Return After Crash'].iloc[-1],DLRaft['Cumulative Return After Crash'].iloc[-1],DREaft['Cumulative Return After Crash'].iloc[-1],EQIXaft['Cumulative Return After Crash'].iloc[-1],EQRaft['Cumulative Return After Crash'].iloc[-1],ESSaft['Cumulative Return After Crash'].iloc[-1],EXRaft['Cumulative Return After Crash'].iloc[-1],FRTaft['Cumulative Return After Crash'].iloc[-1],PEAKaft['Cumulative Return After Crash'].iloc[-1],HSTaft['Cumulative Return After Crash'].iloc[-1],IRMaft['Cumulative Return After Crash'].iloc[-1],KIMaft['Cumulative Return After Crash'].iloc[-1],MAAaft['Cumulative Return After Crash'].iloc[-1],PLDaft['Cumulative Return After Crash'].iloc[-1],PSAaft['Cumulative Return After Crash'].iloc[-1],Oaft['Cumulative Return After Crash'].iloc[-1],REGaft['Cumulative Return After Crash'].iloc[-1],SBACaft['Cumulative Return After Crash'].iloc[-1],SPGaft['Cumulative Return After Crash'].iloc[-1],SLGaft['Cumulative Return After Crash'].iloc[-1],UDRaft['Cumulative Return After Crash'].iloc[-1],VTRaft['Cumulative Return After Crash'].iloc[-1],VNOaft['Cumulative Return After Crash'].iloc[-1],WELLaft['Cumulative Return After Crash'].iloc[-1],WYaft['Cumulative Return After Crash'].iloc[-1]]}

dftot = pd.DataFrame(cumret, columns = ['Cumulative Returns 12/4-6/5', 'Cumulative Returns 12/4-2/20', 'Cumulative Returns 2/20-3/23', 'Cumulative Returns 3/23-6/5'], index= ['S&P 500','Apache','Baker','Cabot','Chevron','Concho','Conoco','Devon','Diamond','EOG','Exxon','Halliburton','Hess','Holly','Kinder','MarathonOil','MarathonPet','National','Noble','Occidental','Oneok','Phillips66','Pioneer','Schlumberger','Technip','Valero','Williams','APD','ALB','AMCR','AVY','BLL','CE','CF','CTVA','DOW','DD','EMN','ECL','FMC','FCX','IP','IFF','LIN','LYB','MLM','MOS','NEM','NUE','PKG','PPG','SEE','SHW','VMC','WRK','MMM','ALK','ALLE','AAL','AME','AOS','BA','CHRW','CAT','CTAS','CPRT','CSX','CMI','DE','DAL','DOV','ETN','EMR','EFX','EXPD','FAST','FDX','FLS','FTV','FBHS','GD','GE','GWW','HON','HWM','HII','IEX','INFO','ITW','IR','J','JBHT','JCI','KSU','LHX','LMT','MAS','NLSN','NSC','NOC','ODFL','PCAR','PH','PNR','PWR','RTX','RSG','RHI','ROK','ROL','ROP','SNA','LUV','SWK','TXT','TT','TDG','UNP','UAL','UPS','URI','VRSK','WAB','WM','XYL','AAP','AMZN','APTV','AZO','BBY','BKNG','BWA','KMX','CCL','CMG','DHI','DRI','DG','DLTR','DPZ','EBAY','EXPE','F','GPS','GRMN','GM','GPC','HRB','HBI','HOG','HAS','HLT','HD','KSS','LB','LVS','LEG','LEN','LKQ','LOW','MAR','MCD','MGM','MHK','NWL','NKE','JWN','NCLH','NVR','ORLY','PHM','PVH','RL','ROST','RCL','SBUX','TPR','TGT','TIF','TJX','TSCO','ULTA','UAA','UA','VFC','WHR','WYNN','YUM','MO','ADM','BF.B','CPB','CHD','CLX','KO','CL','CAG','STZ','COST','COTY','EL','GIS','HSY','HRL','SJM','K','KMB','KHC','KR','LW','MKC','TAP','MDLZ','MNST','PEP','PM','PG','SYY','TSN','WMT','WBA','ABT','ABBV','ABMD','A','ALXN','ALGN','ABC','AMGN','ANTM','BAX','BDX','BIIB','BSX','BMY','CAH','CNC','CERN','CI','COO','CVS','DHR','DVA','XRAY','DXCM','EW','GILD','HCA','HSIC','HOLX','HUM','IDXX','ILMN','INCY','ISRG','IQV','JNJ','LH','LLY','MCK','MDT','MRK','MTD','MYL','PKI','PRGO','PFE','DGX','REGN','RMD','STE','SYK','TFX','TMO','UNH','UHS','VAR','VRTX','WAT','WST','ZBH','ZTS','AFL','ALL','AXP','AIG','AMP','AON','AJG','AIZ','BAC','BK','BRK.B','BLK','COF','CBOE','SCHW','CB','CINF','C','CFG','CME','CMA','DFS','ETFC','RE','FITB','FRC','BEN','GL','GS','HIG','HBAN','ICE','IVZ','JPM','KEY','LNC','L','MTB','MKTX','MMC','MET','MCO','MS','MSCI','NDAQ','NTRS','PBCT','PNC','PFG','PGR','PRU','RJF','RF','SPGI','STT','SIVB','SYF','TROW','TRV','TFC','USB','UNM','WRB','WFC','WLTW','ZION','ACN','ADBE','AMD','AKAM','ADS','APH','ADI','ANSS','AAPL','AMAT','ANET','ADSK','ADP','AVGO','BR','CDNS','CDW','CSCO','CTXS','CTSH','GLW','DXC','FFIV','FIS','FISV','FLT','FLIR','FTNT','IT','GPN','HPE','HPQ','INTC','IBM','INTU','IPGP','JKHY','JNPR','KEYS','KLAC','LRCX','LDOS','MA','MXIM','MCHP','MU','MSFT','MSI','NTAP','NLOK','NVDA','ORCL','PAYX','PAYC','PYPL','QRVO','QCOM','CRM','STX','NOW','SWKS','SNPS','TEL','TXN','VRSN','V','WDC','URI','XRX','XLNX','ZBRA','ATVI','GOOGL','GOOG','T','CTL','CHTR','CMCSA','DISCA','DISCK','DISH','EA','FB','FOXA','FOX','IPG','LYV','NFLX','NWSA','NWS','OMC','TMUS','TTWO','TWTR','VZ','VIAC','DIS','AES','LNT','AEE','AEP','AWK','ATO','CNP','CMS','ED','D','DTE','DUK','EIX','ETR','EVRG','ES','EXC','FE','NEE','NI','NRG','PNW','PPL','PEG','SRE','SO','WEC','XEL','ARE','AMT','AIV','AVB','BXP','CBRE','CCI','DLR','DRE','EQIX','EQR','ESS','EXR','FRT','PEAK','HST','IRM','KIM','MAA','PLD','PSA','O','REG','SBAC','SPG','SLG','UDR','VTR','VNO','WELL','WY'])

pd.DataFrame(dftot)
Out[693]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
S&P 500 1.026077 1.083678 0.663281 1.427519
Apache 0.859358 1.528342 0.150805 3.728538
Baker 0.800631 0.931890 0.451597 1.902465
Cabot 1.268323 0.953416 0.921173 1.444130
Chevron 0.862066 0.939029 0.493762 1.859277
... ... ... ... ...
UDR 0.878265 1.068450 0.592155 1.388149
VTR 0.745649 1.075306 0.368590 1.881304
VNO 0.677924 0.995817 0.476198 1.429598
WELL 0.735259 1.050609 0.494092 1.416417
WY 0.791216 1.042905 0.436346 1.738679

504 rows × 4 columns

In [708]:
dftot.sort_values('Cumulative Returns 3/23-6/5', ascending=False)
Out[708]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
Apache 0.859358 1.528342 0.150805 3.728538
Halliburton 0.676293 1.022357 0.238724 2.770993
LNC 0.834569 1.077507 0.284286 2.724496
Diamond 0.648088 0.974181 0.259557 2.563083
Noble 0.538498 0.887135 0.240970 2.519016
MarathonPet 0.692321 1.003831 0.275805 2.500602
RCL 0.578185 0.914238 0.256740 2.463285
MGM 0.683664 1.017941 0.282931 2.373771
Devon 0.652077 0.989281 0.279007 2.362460
NCLH 0.418392 0.904868 0.199340 2.319545
Oneok 0.640265 1.094451 0.252838 2.313775
Valero 0.800772 0.903015 0.387549 2.288167
URI 1.071946 1.034478 0.455240 2.276203
DFS 0.724860 0.906254 0.351629 2.274672
MarathonOil 0.601479 0.842235 0.326829 2.185075
LKQ 0.864896 0.971722 0.408583 2.178417
LEN 1.082951 1.207377 0.413206 2.170698
PVH 0.644581 0.892450 0.334369 2.160069
Occidental 0.540702 1.117555 0.225506 2.145511
VIAC 0.590308 0.716838 0.385114 2.138298
WHR 0.963461 1.049564 0.430775 2.130956
ALGN 1.037809 0.981203 0.505413 2.092724
DRI 0.725646 1.023145 0.339990 2.086035
Phillips66 0.776406 0.801190 0.466681 2.076503
PHM 0.932216 1.184818 0.380116 2.069899
LB 0.998930 1.252406 0.391546 2.037077
SWK 0.956681 1.057216 0.444492 2.035818
KSS 0.574703 0.968000 0.292383 2.030558
FCX 0.977498 1.087309 0.446192 2.014842
KMX 0.995021 1.056944 0.468106 2.011111
EMN 1.025327 0.988137 0.517874 2.003647
PH 0.989771 1.090191 0.453177 2.003383
SYF 0.680896 0.897545 0.380499 1.993750
Conoco 0.801418 0.971136 0.416950 1.979226
DISH 1.070212 1.224978 0.442683 1.973554
Hess 0.931558 1.057171 0.446693 1.972674
DD 0.894376 0.837183 0.541579 1.972593
Holly 0.724104 0.839110 0.437925 1.970525
GPS 0.766978 1.088474 0.359473 1.960191
PFG 0.891098 1.060227 0.431583 1.947434
ADS 0.578523 0.988111 0.300990 1.945197
BA 0.588895 0.963995 0.314084 1.944991
FITB 0.791709 1.005393 0.407643 1.931743
LOW 1.134332 1.096224 0.535672 1.931711
DHI 1.020600 1.121250 0.476390 1.910690
Baker 0.800631 0.931890 0.451597 1.902465
Williams 0.955264 0.996385 0.505669 1.895964
ABMD 1.288411 0.866883 0.787363 1.887638
ROK 1.166043 1.052818 0.587794 1.884239
AIG 0.711407 0.974369 0.387550 1.883938
VTR 0.745649 1.075306 0.368590 1.881304
AMP 0.960072 1.109109 0.461162 1.877051
FBHS 1.033566 1.155636 0.477189 1.874246
ETFC 1.128149 1.209236 0.498264 1.872387
AAP 0.959102 0.935338 0.550955 1.861147
Chevron 0.862066 0.939029 0.493762 1.859277
EBAY 1.396054 1.077781 0.698859 1.853455
LYB 0.818430 0.911128 0.485783 1.849099
Concho 0.950866 1.100975 0.467544 1.847226
APTV 0.929414 0.986143 0.510484 1.846237
COF 0.796171 1.043224 0.414043 1.843247
CFG 0.762513 1.010949 0.409232 1.843100
PAYC 1.221815 1.149754 0.576612 1.842964
GPC 0.889274 0.959911 0.504869 1.834957
ECL 1.248423 1.121982 0.606951 1.833253
PNR 0.952229 1.013421 0.512907 1.831947
F 0.820112 0.897207 0.499377 1.830424
EXPE 0.881584 1.152520 0.417968 1.830089
PYPL 1.485714 1.166000 0.696398 1.829697
CTAS 1.123750 1.167680 0.527578 1.824144
HUM 1.132919 1.078083 0.577994 1.818122
SWKS 1.377859 1.189031 0.637872 1.816678
AAL 0.678963 1.041271 0.359523 1.813659
CBRE 0.947221 1.116956 0.468289 1.810929
Pioneer 0.849667 1.092100 0.430015 1.809267
TXT 0.868561 1.057504 0.454755 1.806094
CCL 0.490201 0.968551 0.282353 1.792500
CMG 1.301532 1.146022 0.634666 1.789438
UNM 0.640345 1.038621 0.344622 1.789017
HOLX 1.005178 1.010740 0.557495 1.783867
PWR 1.053429 0.979622 0.603754 1.781092
MS 1.014128 1.100532 0.517395 1.781014
IQV 1.030591 1.130908 0.511758 1.780714
ALK 0.638374 0.976689 0.367656 1.777778
MAS 1.067149 0.996270 0.606388 1.766437
SPG 0.594073 0.942269 0.356934 1.766348
PRU 0.762781 1.033275 0.418213 1.765171
WYNN 0.855735 1.122874 0.431786 1.764982
BWA 0.889127 0.838102 0.601707 1.763121
EMR 0.912925 0.979864 0.528742 1.762080
ULTA 1.052834 1.258572 0.475343 1.759844
MKTX 1.253585 0.874642 0.816338 1.755714
CMI 1.011065 0.938665 0.614888 1.751752
GM 0.855028 0.985754 0.498725 1.739205
WY 0.791216 1.042905 0.436346 1.738679
SPGI 1.241787 1.130187 0.632972 1.735850
DXCM 1.590409 1.319967 0.695184 1.733191
MET 0.837238 1.074394 0.450077 1.731407
RF 0.799161 0.991607 0.466747 1.726684
TFC 0.831176 1.037516 0.464473 1.724796
LH 1.046095 1.098882 0.552513 1.722968
WAB 0.924163 1.030858 0.521690 1.718454
STZ 0.984427 1.130501 0.507056 1.717342
EOG 0.823178 1.054684 0.454534 1.717140
MHK 0.805830 0.995105 0.472013 1.715617
Schlumberger 0.607626 0.934789 0.379249 1.713952
YUM 0.972984 1.047077 0.544142 1.707714
IR 0.995806 1.157879 0.504528 1.704615
IDXX 1.237632 1.117486 0.649991 1.703892
KEY 0.762703 1.048717 0.427572 1.700935
ZBH 0.926063 1.067534 0.510196 1.700285
BLK 1.143500 1.164848 0.577542 1.699743
SYY 0.751189 0.935602 0.472820 1.698098
RHI 1.004337 1.028105 0.576612 1.694176
MCO 1.233201 1.229585 0.591996 1.694167
EFX 1.297537 1.183331 0.648083 1.691933
Exxon 0.773197 0.871959 0.525393 1.687758
SNPS 1.345269 1.140210 0.699510 1.686670
FTV 0.988797 1.075340 0.547076 1.680790
MCHP 1.139424 1.120054 0.605845 1.679133
NVDA 1.703753 1.474071 0.688986 1.677559
TFX 1.064959 1.086960 0.586195 1.671386
AIV 0.799280 1.044499 0.458484 1.669039
QRVO 1.111891 0.976257 0.682946 1.667676
AME 1.002999 1.039814 0.578916 1.666209
TEL 1.005967 1.026740 0.588356 1.665264
CMA 0.649173 0.904592 0.431026 1.664960
C 0.792941 1.053752 0.452442 1.663182
DXC 0.494913 0.835854 0.356579 1.660517
HOG 0.706700 0.997220 0.427655 1.657106
NRG 0.929150 0.987854 0.567623 1.657040
ALB 1.339955 1.498887 0.539709 1.656385
DOW 0.841048 0.932670 0.545119 1.654251
KLAC 1.201048 1.043929 0.695517 1.654177
LEG 0.716766 0.871889 0.497234 1.653316
ETN 1.047300 1.153424 0.549667 1.651895
ANTM 1.001775 1.022827 0.594271 1.648100
DOV 0.974461 1.077884 0.549235 1.646015
NDAQ 1.139626 1.088205 0.636387 1.645622
FLT 0.922341 1.036511 0.541038 1.644712
AMCR 1.068762 1.008841 0.644596 1.643505
SIVB 1.021942 1.176581 0.528860 1.642343
FLS 0.682437 1.001296 0.415102 1.641892
AVGO 1.023763 0.998450 0.625631 1.638910
SYK 1.013016 1.098351 0.563550 1.636601
CDNS 1.386319 1.167450 0.725737 1.636233
STT 0.948102 1.044522 0.554757 1.636195
TPR 0.677760 1.128034 0.367234 1.636106
NKE 1.095924 1.094003 0.612504 1.635510
FTNT 1.356377 1.139326 0.727966 1.635390
NSC 1.036774 1.125160 0.563920 1.634001
GPN 1.076320 1.184691 0.556771 1.631774
ROST 0.897766 1.084625 0.508198 1.628735
BBY 1.042309 1.161026 0.551398 1.628132
TDG 0.871723 1.126679 0.475359 1.627632
ZBRA 1.096126 0.958572 0.703362 1.625761
ADSK 1.324887 1.171599 0.697440 1.621410
UNP 1.077977 1.069637 0.621810 1.620747
INFO 1.014621 1.109517 0.564396 1.620264
ORLY 0.956140 0.893890 0.660256 1.620037
KIM 0.715562 0.891396 0.496308 1.617428
KSU 1.089471 1.179345 0.571347 1.616869
PPL 0.869213 1.024595 0.524993 1.615923
UAL 0.481003 0.906317 0.328495 1.615619
GS 1.009169 1.077753 0.579942 1.614581
ISRG 1.017911 1.061752 0.594786 1.611856
CI 1.064755 1.120311 0.590082 1.610641
FMC 1.065009 1.117429 0.592668 1.608133
NVR 0.922746 1.067247 0.538664 1.605090
ZION 0.786194 0.941763 0.520649 1.603405
FLIR 0.884381 1.108381 0.497852 1.602692
NWS 0.997705 1.146136 0.543391 1.601966
UNH 1.116461 1.081663 0.644954 1.600380
NUE 0.814437 0.857066 0.593939 1.599930
ROP 1.170821 1.107870 0.662222 1.595871
AZO 0.992125 0.930728 0.668089 1.595545
WST 1.394024 1.163233 0.751209 1.595301
SNA 0.956210 1.004900 0.597499 1.592550
AXP 0.930388 1.158046 0.504906 1.591212
FRC 1.089334 1.067386 0.642366 1.588757
PAYX 0.946665 1.067053 0.558462 1.588609
IT 0.855893 0.982375 0.548693 1.587862
PNC 0.845920 1.020680 0.522009 1.587674
SEE 0.934806 0.929750 0.633371 1.587438
LRCX 1.159965 1.249629 0.587080 1.581125
AFL 0.774508 0.983948 0.498932 1.577657
CSX 1.065720 1.141945 0.592418 1.575325
NWSA 1.009346 1.133178 0.565636 1.574727
BKNG 0.943332 1.025698 0.584623 1.573145
XRAY 0.871332 0.997395 0.555362 1.573041
PPG 0.882649 0.924555 0.607406 1.571724
CE 0.800262 0.891819 0.571166 1.571061
KHC 1.051241 0.883661 0.757476 1.570534
HBAN 0.740067 0.952189 0.495049 1.570000
HD 1.190565 1.153760 0.657396 1.569678
AVY 0.959465 1.064110 0.575784 1.565968
TSCO 1.275909 1.034267 0.788485 1.564566
MLM 0.836382 0.957519 0.558432 1.564183
CPRT 1.054796 1.117748 0.604111 1.562096
PCAR 0.976328 0.946142 0.660710 1.561811
HAS 0.812990 0.994168 0.524575 1.558897
DGX 1.101599 1.061524 0.665899 1.558424
FB 1.161341 1.079865 0.690186 1.558204
TJX 0.956246 1.066633 0.575544 1.557671
BAC 0.847964 1.051282 0.518795 1.554757
NWL 0.867266 1.051824 0.530417 1.554502
HON 0.931644 1.043242 0.574765 1.553726
APH 1.038936 1.006991 0.665317 1.550725
ITW 1.065428 1.106968 0.621526 1.548565
National 0.660659 1.038242 0.412786 1.541538
GWW 1.018809 0.979234 0.675143 1.541028
IPGP 1.183037 0.983386 0.781004 1.540356
MAR 0.775241 1.054431 0.477595 1.539427
BK 0.867159 0.939524 0.599825 1.538741
PKI 1.130115 1.009959 0.727224 1.538688
DAL 0.609347 1.041384 0.380610 1.537354
MA 1.087296 1.200844 0.590216 1.534088
JCI 0.855685 1.002611 0.556345 1.534043
HRB 0.811030 1.012399 0.522804 1.532310
NOW 1.426077 1.289043 0.723409 1.529296
MOS 0.767556 1.005444 0.499188 1.529284
RTX 0.792411 1.037227 0.500000 1.527942
JBHT 1.080061 0.983630 0.718807 1.527581
FOXA 0.869014 1.043099 0.545504 1.527228
FOX 0.873268 1.054850 0.542146 1.527007
LYV 0.819804 1.088946 0.493391 1.525850
HSIC 0.951988 0.996674 0.626524 1.524548
Technip 0.500783 0.900261 0.365429 1.522222
ZTS 1.177635 1.198848 0.645489 1.521800
GD 0.910939 1.044194 0.573272 1.521764
STE 1.063867 1.092231 0.640361 1.521065
HIG 0.767599 0.955263 0.528753 1.519700
UHS 0.774075 0.992980 0.513033 1.519488
GRMN 1.002909 1.036987 0.637511 1.517052
Kinder 0.866321 1.152332 0.495953 1.515866
FAST 1.216771 1.112949 0.722194 1.513840
VMC 0.877802 0.960257 0.604021 1.513411
PLD 1.050604 1.086169 0.640424 1.510336
CNP 0.738269 1.040453 0.470451 1.508264
BR 1.040325 0.996879 0.694184 1.503323
BSX 0.902813 0.988142 0.607765 1.503291
GLW 0.952177 1.009636 0.627430 1.503099
SCHW 0.873343 0.990822 0.586661 1.502456
IPG 0.874774 1.127034 0.516647 1.502329
TROW 1.053894 1.131614 0.620154 1.501756
MKC 0.996849 0.956002 0.694806 1.500747
DE 1.008774 1.003388 0.670265 1.499955
FRT 0.763345 0.974800 0.522287 1.499325
AMAT 1.073097 1.183812 0.604970 1.498382
LDOS 1.181747 1.329062 0.593863 1.497247
DTE 0.917053 1.069376 0.573423 1.495508
LW 0.846904 1.128254 0.502634 1.493398
CB 0.931219 1.080034 0.577383 1.493312
ILMN 1.107701 0.939877 0.791320 1.489359
VFC 0.775897 0.953998 0.547565 1.485323
CF 0.730847 0.881840 0.558257 1.484573
LMT 1.068048 1.107624 0.649811 1.483923
BEN 0.857575 0.969777 0.596026 1.483660
ODFL 1.399741 1.241721 0.761386 1.480537
INCY 0.984316 0.871263 0.763320 1.480057
INTU 1.133176 1.184818 0.646395 1.479612
AMT 1.241517 1.160199 0.723479 1.479089
GL 0.845503 1.093879 0.523120 1.477558
AAPL 1.266524 1.223733 0.700500 1.477470
RJF 0.916094 1.140444 0.544144 1.476225
SHW 1.024470 1.022352 0.679188 1.475397
LIN 1.091393 1.090999 0.678119 1.475200
IP 0.849217 0.934508 0.616298 1.474499
VAR 0.955464 1.047135 0.619080 1.473889
WRB 0.935952 1.144218 0.555399 1.472786
AJG 1.085008 1.168395 0.630859 1.472011
FFIV 1.010865 0.925268 0.742567 1.471262
PEAK 0.853446 1.084327 0.534996 1.471178
CAT 0.964316 0.977234 0.670781 1.471094
V 1.105260 1.170819 0.641948 1.470532
MAA 0.923488 1.096104 0.573257 1.469706
ADP 0.949932 1.072373 0.603363 1.468140
ALLE 0.947122 1.066180 0.605314 1.467557
HCA 0.821848 1.053339 0.531767 1.467242
USB 0.714382 0.923959 0.527920 1.464570
CDW 0.926180 1.021503 0.619213 1.464252
ICE 1.048114 1.020253 0.702132 1.463126
ADI 1.089169 1.088819 0.683741 1.463011
IFF 0.984574 0.947650 0.710528 1.462240
AOS 1.121415 0.960104 0.799641 1.460674
IEX 1.063104 1.103076 0.660502 1.459136
MTD 1.174781 1.032433 0.780272 1.458306
REG 0.786743 0.992102 0.544490 1.456422
DIS 0.841786 0.946655 0.610957 1.455457
ABBV 1.076261 1.080619 0.684495 1.455039
MMC 1.040795 1.092980 0.655022 1.453775
ANET 1.214094 1.178524 0.709129 1.452743
SBUX 0.961827 1.037471 0.638262 1.452520
ACN 1.044644 1.071751 0.671763 1.450971
PEG 0.907517 1.004242 0.622846 1.450895
ADM 0.990450 1.030282 0.662672 1.450699
PKG 0.994423 0.891347 0.769122 1.450538
BF.B 0.993809 1.054688 0.649616 1.450516
JWN 0.600318 1.090234 0.379990 1.449071
Cabot 1.268323 0.953416 0.921173 1.444130
CCI 1.253252 1.241285 0.700521 1.441272
ESS 0.842950 1.047137 0.558799 1.440597
ALL 0.957599 1.120937 0.593148 1.440257
HBI 0.877717 1.000000 0.610054 1.438753
JNPR 1.023055 1.001235 0.710526 1.438079
MCD 1.014667 1.106891 0.637437 1.438074
NTRS 0.852925 0.965770 0.614508 1.437173
O 0.819199 1.078615 0.529117 1.435395
SRE 0.906559 1.083412 0.584277 1.432134
TT 0.793358 1.135558 0.488049 1.431518
ABT 1.054942 1.038507 0.710151 1.430436
VNO 0.677924 0.995817 0.476198 1.429598
CNC 1.085529 1.095865 0.692986 1.429418
MSCI 1.277381 1.241980 0.719748 1.428979
S&P 500 1.026077 1.083678 0.663281 1.427519
DHR 1.175955 1.087910 0.757457 1.427053
AVB 0.802016 1.054941 0.533040 1.426247
A 1.116768 1.042135 0.751364 1.426227
BLL 1.124411 1.165019 0.678068 1.423373
FISV 0.954351 1.061416 0.631976 1.422727
EL 1.036893 1.067974 0.683229 1.421042
MMM 1.001615 0.948666 0.743378 1.420294
ALXN 0.997921 0.888177 0.791203 1.420067
D 1.024802 1.085106 0.665434 1.419263
EW 0.906251 0.950120 0.672182 1.419004
HPE 0.705845 0.919314 0.541120 1.418902
MNST 1.196059 1.165756 0.724006 1.417108
WELL 0.735259 1.050609 0.494092 1.416417
QCOM 1.078826 1.090034 0.698893 1.416120
J 1.032101 1.182408 0.617563 1.413426
TWTR 1.162333 1.301667 0.632266 1.412313
WDC 1.021501 1.455734 0.497683 1.409951
JPM 0.840487 1.038915 0.574805 1.407440
TRV 0.920027 0.991196 0.659501 1.407424
APD 1.085882 1.115674 0.691973 1.406552
MU 1.160259 1.274298 0.648305 1.404444
EVRG 0.996838 1.162820 0.610658 1.403829
DRE 1.049615 1.102652 0.678821 1.402286
WLTW 1.070002 1.083920 0.704344 1.401532
NEE 1.085888 1.182575 0.655741 1.400308
MTB 0.751829 1.033720 0.519672 1.399546
MDT 0.911560 1.017152 0.640436 1.399342
CTVA 1.233897 1.270934 0.694330 1.398266
VRSN 1.131049 1.119165 0.723094 1.397632
CAH 1.045455 1.102957 0.678583 1.396829
FIS 1.054000 1.155002 0.653463 1.396486
MCK 1.130526 1.194600 0.677773 1.396285
DUK 1.006071 1.140304 0.632456 1.395012
EXPD 1.065354 1.031857 0.740162 1.394916
IBM 1.000530 1.145693 0.626703 1.393479
ES 1.042159 1.152132 0.650601 1.390326
CHTR 1.148620 1.167017 0.708023 1.390120
CTSH 0.926603 1.114744 0.598189 1.389570
UDR 0.878265 1.068450 0.592155 1.388149
JKHY 1.190404 1.154600 0.742921 1.387779
ANSS 1.146585 1.166098 0.708625 1.387569
TMO 1.122164 1.064694 0.759686 1.387387
EXR 0.970784 1.008079 0.695275 1.385069
CSCO 1.089770 1.067441 0.738527 1.382370
ARE 0.990754 1.054039 0.681128 1.380003
SBAC 1.262661 1.241205 0.737548 1.379281
UA 0.578483 0.878307 0.477912 1.378151
UAA 0.596454 0.886620 0.488485 1.377171
CME 0.938275 1.020607 0.667655 1.376953
HLT 0.837013 1.075062 0.565446 1.376914
EQIX 1.196419 1.151813 0.754392 1.376906
MSFT 1.249249 1.230697 0.737339 1.376673
AON 0.990060 1.158845 0.620834 1.376134
ROL 1.236617 1.138098 0.789842 1.375673
VRSK 1.142494 1.164913 0.715391 1.370936
RL 0.824087 1.121170 0.536681 1.369572
EIX 0.857465 1.089014 0.575142 1.369013
IVZ 0.621122 1.075776 0.422074 1.367937
GOOGL 1.091801 1.150159 0.694883 1.366074
LHX 1.057776 1.155514 0.671224 1.363800
NLSN 0.835131 1.142784 0.536629 1.361809
GOOG 1.089244 1.149643 0.695992 1.361313
NTAP 0.809909 0.905123 0.658164 1.359547
DISCK 0.746742 0.955563 0.575524 1.357837
RSG 1.021588 1.126236 0.668483 1.356927
AWK 1.074047 1.120908 0.706992 1.355311
SO 0.932366 1.113575 0.619252 1.352070
EXC 0.906152 1.119219 0.601404 1.346231
DVA 1.216648 1.166621 0.776610 1.342866
DISCA 0.769565 0.931056 0.616744 1.340184
ABC 1.120077 1.092463 0.765876 1.338699
SLG 0.641268 1.097052 0.436916 1.337871
MXIM 1.085562 1.105934 0.734102 1.337115
XLNX 1.066874 0.995786 0.801871 1.336111
TMUS 1.290911 1.270105 0.760804 1.335931
MO 0.836360 0.909200 0.688762 1.335564
TXN 1.092784 1.093033 0.748764 1.335230
CERN 1.034136 1.086544 0.713075 1.334735
STX 0.927524 0.933663 0.744840 1.333742
DG 1.209118 1.079323 0.843963 1.327375
ETR 0.878249 1.112946 0.594751 1.326810
IRM 0.912750 1.054685 0.652490 1.326339
JNJ 1.056823 1.064500 0.749073 1.325355
RMD 1.071195 1.172161 0.689545 1.325313
URI 0.917414 0.990284 0.700000 1.323450
BMY 1.041256 1.106452 0.711984 1.321767
HII 0.795953 0.932888 0.648513 1.315646
PRGO 1.097061 1.175972 0.709109 1.315590
RE 0.845421 1.076374 0.597229 1.315131
NEM 1.399438 1.229236 0.866112 1.314450
L 0.799509 1.102496 0.551865 1.314055
VRTX 1.186593 1.094636 0.825010 1.313931
CHRW 1.087315 0.979448 0.845229 1.313409
HST 0.798291 0.978917 0.621653 1.311798
KEYS 1.031504 0.954440 0.824396 1.310952
REGN 1.613644 1.078844 1.141529 1.310274
WEC 1.042205 1.149287 0.692353 1.309776
BXP 0.739420 1.064638 0.530425 1.309380
DLR 1.186230 1.144416 0.791636 1.309360
COO 1.020667 1.136948 0.686075 1.308495
KO 0.904218 1.100018 0.628935 1.306975
XEL 1.052411 1.139655 0.707514 1.305200
AMZN 1.410243 1.222873 0.883763 1.304899
FE 0.897904 1.074704 0.640857 1.303706
HWM 0.565963 0.960092 NaN 1.300000
ATO 0.977007 1.114403 0.675166 1.298509
INTC 1.148518 1.168333 0.757525 1.297701
CAG 1.137121 1.016672 0.862115 1.297360
EQR 0.788131 1.025519 0.594792 1.292080
WRK 0.813865 1.029710 0.611837 1.291818
GIS 1.144465 1.015572 0.873453 1.290186
GE 0.722936 1.149541 0.487630 1.289689
CVS 0.911333 0.959200 0.737976 1.287436
WAT 0.928784 0.966170 0.747032 1.286832
PSA 0.969101 1.044283 0.722232 1.284914
XYL 0.965110 1.176174 0.638845 1.284428
ADBE 1.298800 1.252719 0.810824 1.278680
CBOE 0.813706 1.021540 0.623138 1.278286
WM 0.994152 1.099149 0.708343 1.276886
AMD 1.337868 1.442933 0.727082 1.275216
FDX 0.924222 1.071749 0.677703 1.272459
HPQ 0.868762 1.129740 0.605124 1.270803
NLOK 0.829393 0.819149 0.797499 1.269602
NOC 0.996766 1.058390 0.742904 1.267694
LNT 0.952417 1.120369 0.671311 1.266317
PM 0.924604 1.065043 0.685956 1.265589
PFE 0.943629 0.939958 0.794700 1.263250
PNW 0.913043 1.162756 0.622317 1.261803
PEP 0.964603 1.057247 0.724580 1.259175
OMC 0.772889 1.001780 0.613100 1.258385
PBCT 0.825660 1.003683 0.654434 1.257009
TGT 0.975988 0.949727 0.817605 1.256903
WFC 0.594853 0.889181 0.533376 1.254257
LLY 1.257352 1.196596 0.838380 1.253339
MDLZ 0.984814 1.106862 0.710196 1.252802
MSI 0.944047 1.123895 0.671121 1.251606
CTL 0.758065 0.937588 0.649963 1.243959
BAX 1.082009 1.128958 0.771653 1.242028
KMB 1.013019 1.034039 0.790181 1.239808
EA 1.175793 1.085163 0.874267 1.239342
MRK 0.927710 0.930303 0.804946 1.238855
BRK.B 0.918773 1.044734 0.710567 1.237649
CRM 1.111552 1.236080 0.726727 1.237404
AIZ 0.889853 1.096592 0.658190 1.232883
CMCSA 0.967364 1.057688 0.742503 1.231782
AEE 1.016407 1.151963 0.716437 1.231546
PGR 1.139616 1.125278 0.823231 1.230203
ATVI 1.288604 1.187825 0.882344 1.229502
TAP 0.829259 1.083708 0.623714 1.226852
CMS 0.963215 1.111489 0.707246 1.225314
K 0.998326 0.999087 0.815593 1.225168
T 0.860105 1.013386 0.693344 1.224131
AEP 0.929154 1.112463 0.683727 1.221571
ORCL 0.992097 1.020217 0.796073 1.221543
LVS 0.844951 1.075770 0.644276 1.219102
DLTR 1.040780 1.044365 0.819245 1.216446
HSY 0.901971 1.066263 0.695959 1.215472
AMGN 0.964897 0.949803 0.836995 1.213736
CHD 1.052039 1.087967 0.797667 1.212256
PG 0.950213 1.016462 0.771844 1.211157
BDX 0.940831 0.985344 0.791367 1.206551
AES 0.756614 1.100529 0.570673 1.204718
NI 0.946415 1.133962 0.694176 1.202301
CL 1.063455 1.103995 0.804550 1.197288
TTWO 1.056986 0.943998 0.953878 1.173830
NFLX 1.378812 1.268402 0.933342 1.164682
DPZ 1.316119 1.304755 0.867912 1.162226
CLX 1.314504 1.092282 1.036365 1.161220
UPS 0.915686 0.914227 0.863073 1.160501
AKAM 1.145436 1.174014 0.843008 1.157353
ED 0.868057 1.089882 0.690949 1.152716
VZ 0.953907 0.959689 0.866070 1.147684
XRX 0.481356 0.959061 0.438010 1.145872
CTXS 1.255073 1.078470 1.016977 1.144326
BIIB 1.059409 1.162752 0.799638 1.139418
HRL 1.060773 1.006188 0.929277 1.134484
LUV 0.684475 1.020796 0.592378 1.131930
TSN 0.727476 0.883012 0.730964 1.127083
MYL 0.935911 1.108581 0.752031 1.122618
CPB 0.994017 0.984939 0.899246 1.122292
SJM 1.011010 1.040369 0.866407 1.121620
COST 1.052341 1.092945 0.881048 1.092845
WMT 1.024181 0.991575 0.971026 1.063703
GILD 1.149813 1.003745 1.083731 1.057017
KR 1.175109 1.077424 1.043653 1.045045
WBA 0.769818 0.880496 0.840756 1.039899
CINF 0.657867 1.072406 0.595612 1.029949
TIF 0.912279 1.003515 0.906699 1.002630
COTY 0.438687 0.986183 0.450963 0.986408
In [707]:
dftot.sort_values('Cumulative Returns 12/4-2/20', ascending=False)
Out[707]:
Cumulative Returns 12/4-6/5 Cumulative Returns 12/4-2/20 Cumulative Returns 2/20-3/23 Cumulative Returns 3/23-6/5
Apache 0.859358 1.528342 0.150805 3.728538
ALB 1.339955 1.498887 0.539709 1.656385
NVDA 1.703753 1.474071 0.688986 1.677559
WDC 1.021501 1.455734 0.497683 1.409951
AMD 1.337868 1.442933 0.727082 1.275216
LDOS 1.181747 1.329062 0.593863 1.497247
DXCM 1.590409 1.319967 0.695184 1.733191
DPZ 1.316119 1.304755 0.867912 1.162226
TWTR 1.162333 1.301667 0.632266 1.412313
NOW 1.426077 1.289043 0.723409 1.529296
MU 1.160259 1.274298 0.648305 1.404444
CTVA 1.233897 1.270934 0.694330 1.398266
TMUS 1.290911 1.270105 0.760804 1.335931
NFLX 1.378812 1.268402 0.933342 1.164682
ULTA 1.052834 1.258572 0.475343 1.759844
ADBE 1.298800 1.252719 0.810824 1.278680
LB 0.998930 1.252406 0.391546 2.037077
LRCX 1.159965 1.249629 0.587080 1.581125
MSCI 1.277381 1.241980 0.719748 1.428979
ODFL 1.399741 1.241721 0.761386 1.480537
CCI 1.253252 1.241285 0.700521 1.441272
SBAC 1.262661 1.241205 0.737548 1.379281
CRM 1.111552 1.236080 0.726727 1.237404
MSFT 1.249249 1.230697 0.737339 1.376673
MCO 1.233201 1.229585 0.591996 1.694167
NEM 1.399438 1.229236 0.866112 1.314450
DISH 1.070212 1.224978 0.442683 1.973554
AAPL 1.266524 1.223733 0.700500 1.477470
AMZN 1.410243 1.222873 0.883763 1.304899
ETFC 1.128149 1.209236 0.498264 1.872387
LEN 1.082951 1.207377 0.413206 2.170698
MA 1.087296 1.200844 0.590216 1.534088
ZTS 1.177635 1.198848 0.645489 1.521800
LLY 1.257352 1.196596 0.838380 1.253339
MCK 1.130526 1.194600 0.677773 1.396285
SWKS 1.377859 1.189031 0.637872 1.816678
ATVI 1.288604 1.187825 0.882344 1.229502
PHM 0.932216 1.184818 0.380116 2.069899
INTU 1.133176 1.184818 0.646395 1.479612
GPN 1.076320 1.184691 0.556771 1.631774
AMAT 1.073097 1.183812 0.604970 1.498382
EFX 1.297537 1.183331 0.648083 1.691933
NEE 1.085888 1.182575 0.655741 1.400308
J 1.032101 1.182408 0.617563 1.413426
KSU 1.089471 1.179345 0.571347 1.616869
ANET 1.214094 1.178524 0.709129 1.452743
SIVB 1.021942 1.176581 0.528860 1.642343
XYL 0.965110 1.176174 0.638845 1.284428
PRGO 1.097061 1.175972 0.709109 1.315590
AKAM 1.145436 1.174014 0.843008 1.157353
RMD 1.071195 1.172161 0.689545 1.325313
ADSK 1.324887 1.171599 0.697440 1.621410
V 1.105260 1.170819 0.641948 1.470532
AJG 1.085008 1.168395 0.630859 1.472011
INTC 1.148518 1.168333 0.757525 1.297701
CTAS 1.123750 1.167680 0.527578 1.824144
CDNS 1.386319 1.167450 0.725737 1.636233
CHTR 1.148620 1.167017 0.708023 1.390120
DVA 1.216648 1.166621 0.776610 1.342866
ANSS 1.146585 1.166098 0.708625 1.387569
PYPL 1.485714 1.166000 0.696398 1.829697
MNST 1.196059 1.165756 0.724006 1.417108
BLL 1.124411 1.165019 0.678068 1.423373
VRSK 1.142494 1.164913 0.715391 1.370936
BLK 1.143500 1.164848 0.577542 1.699743
WST 1.394024 1.163233 0.751209 1.595301
EVRG 0.996838 1.162820 0.610658 1.403829
PNW 0.913043 1.162756 0.622317 1.261803
BIIB 1.059409 1.162752 0.799638 1.139418
BBY 1.042309 1.161026 0.551398 1.628132
AMT 1.241517 1.160199 0.723479 1.479089
AON 0.990060 1.158845 0.620834 1.376134
AXP 0.930388 1.158046 0.504906 1.591212
IR 0.995806 1.157879 0.504528 1.704615
FBHS 1.033566 1.155636 0.477189 1.874246
LHX 1.057776 1.155514 0.671224 1.363800
FIS 1.054000 1.155002 0.653463 1.396486
JKHY 1.190404 1.154600 0.742921 1.387779
HD 1.190565 1.153760 0.657396 1.569678
ETN 1.047300 1.153424 0.549667 1.651895
EXPE 0.881584 1.152520 0.417968 1.830089
Kinder 0.866321 1.152332 0.495953 1.515866
ES 1.042159 1.152132 0.650601 1.390326
AEE 1.016407 1.151963 0.716437 1.231546
EQIX 1.196419 1.151813 0.754392 1.376906
GOOGL 1.091801 1.150159 0.694883 1.366074
PAYC 1.221815 1.149754 0.576612 1.842964
GOOG 1.089244 1.149643 0.695992 1.361313
GE 0.722936 1.149541 0.487630 1.289689
WEC 1.042205 1.149287 0.692353 1.309776
NWS 0.997705 1.146136 0.543391 1.601966
CMG 1.301532 1.146022 0.634666 1.789438
IBM 1.000530 1.145693 0.626703 1.393479
DLR 1.186230 1.144416 0.791636 1.309360
WRB 0.935952 1.144218 0.555399 1.472786
NLSN 0.835131 1.142784 0.536629 1.361809
CSX 1.065720 1.141945 0.592418 1.575325
RJF 0.916094 1.140444 0.544144 1.476225
DUK 1.006071 1.140304 0.632456 1.395012
SNPS 1.345269 1.140210 0.699510 1.686670
XEL 1.052411 1.139655 0.707514 1.305200
FTNT 1.356377 1.139326 0.727966 1.635390
ROL 1.236617 1.138098 0.789842 1.375673
COO 1.020667 1.136948 0.686075 1.308495
TT 0.793358 1.135558 0.488049 1.431518
NI 0.946415 1.133962 0.694176 1.202301
NWSA 1.009346 1.133178 0.565636 1.574727
TROW 1.053894 1.131614 0.620154 1.501756
IQV 1.030591 1.130908 0.511758 1.780714
STZ 0.984427 1.130501 0.507056 1.717342
SPGI 1.241787 1.130187 0.632972 1.735850
HPQ 0.868762 1.129740 0.605124 1.270803
BAX 1.082009 1.128958 0.771653 1.242028
LW 0.846904 1.128254 0.502634 1.493398
TPR 0.677760 1.128034 0.367234 1.636106
IPG 0.874774 1.127034 0.516647 1.502329
TDG 0.871723 1.126679 0.475359 1.627632
RSG 1.021588 1.126236 0.668483 1.356927
PGR 1.139616 1.125278 0.823231 1.230203
NSC 1.036774 1.125160 0.563920 1.634001
MSI 0.944047 1.123895 0.671121 1.251606
WYNN 0.855735 1.122874 0.431786 1.764982
ECL 1.248423 1.121982 0.606951 1.833253
DHI 1.020600 1.121250 0.476390 1.910690
RL 0.824087 1.121170 0.536681 1.369572
ALL 0.957599 1.120937 0.593148 1.440257
AWK 1.074047 1.120908 0.706992 1.355311
LNT 0.952417 1.120369 0.671311 1.266317
CI 1.064755 1.120311 0.590082 1.610641
MCHP 1.139424 1.120054 0.605845 1.679133
EXC 0.906152 1.119219 0.601404 1.346231
VRSN 1.131049 1.119165 0.723094 1.397632
CPRT 1.054796 1.117748 0.604111 1.562096
Occidental 0.540702 1.117555 0.225506 2.145511
IDXX 1.237632 1.117486 0.649991 1.703892
FMC 1.065009 1.117429 0.592668 1.608133
CBRE 0.947221 1.116956 0.468289 1.810929
APD 1.085882 1.115674 0.691973 1.406552
CTSH 0.926603 1.114744 0.598189 1.389570
ATO 0.977007 1.114403 0.675166 1.298509
SO 0.932366 1.113575 0.619252 1.352070
FAST 1.216771 1.112949 0.722194 1.513840
ETR 0.878249 1.112946 0.594751 1.326810
AEP 0.929154 1.112463 0.683727 1.221571
CMS 0.963215 1.111489 0.707246 1.225314
INFO 1.014621 1.109517 0.564396 1.620264
AMP 0.960072 1.109109 0.461162 1.877051
MYL 0.935911 1.108581 0.752031 1.122618
FLIR 0.884381 1.108381 0.497852 1.602692
ROP 1.170821 1.107870 0.662222 1.595871
LMT 1.068048 1.107624 0.649811 1.483923
ITW 1.065428 1.106968 0.621526 1.548565
MCD 1.014667 1.106891 0.637437 1.438074
MDLZ 0.984814 1.106862 0.710196 1.252802
BMY 1.041256 1.106452 0.711984 1.321767
MXIM 1.085562 1.105934 0.734102 1.337115
CL 1.063455 1.103995 0.804550 1.197288
IEX 1.063104 1.103076 0.660502 1.459136
CAH 1.045455 1.102957 0.678583 1.396829
DRE 1.049615 1.102652 0.678821 1.402286
L 0.799509 1.102496 0.551865 1.314055
Concho 0.950866 1.100975 0.467544 1.847226
MS 1.014128 1.100532 0.517395 1.781014
AES 0.756614 1.100529 0.570673 1.204718
KO 0.904218 1.100018 0.628935 1.306975
WM 0.994152 1.099149 0.708343 1.276886
LH 1.046095 1.098882 0.552513 1.722968
SYK 1.013016 1.098351 0.563550 1.636601
SLG 0.641268 1.097052 0.436916 1.337871
AIZ 0.889853 1.096592 0.658190 1.232883
LOW 1.134332 1.096224 0.535672 1.931711
MAA 0.923488 1.096104 0.573257 1.469706
CNC 1.085529 1.095865 0.692986 1.429418
VRTX 1.186593 1.094636 0.825010 1.313931
Oneok 0.640265 1.094451 0.252838 2.313775
NKE 1.095924 1.094003 0.612504 1.635510
GL 0.845503 1.093879 0.523120 1.477558
TXN 1.092784 1.093033 0.748764 1.335230
MMC 1.040795 1.092980 0.655022 1.453775
COST 1.052341 1.092945 0.881048 1.092845
ABC 1.120077 1.092463 0.765876 1.338699
CLX 1.314504 1.092282 1.036365 1.161220
STE 1.063867 1.092231 0.640361 1.521065
Pioneer 0.849667 1.092100 0.430015 1.809267
LIN 1.091393 1.090999 0.678119 1.475200
JWN 0.600318 1.090234 0.379990 1.449071
PH 0.989771 1.090191 0.453177 2.003383
QCOM 1.078826 1.090034 0.698893 1.416120
ED 0.868057 1.089882 0.690949 1.152716
EIX 0.857465 1.089014 0.575142 1.369013
LYV 0.819804 1.088946 0.493391 1.525850
ADI 1.089169 1.088819 0.683741 1.463011
GPS 0.766978 1.088474 0.359473 1.960191
NDAQ 1.139626 1.088205 0.636387 1.645622
CHD 1.052039 1.087967 0.797667 1.212256
DHR 1.175955 1.087910 0.757457 1.427053
FCX 0.977498 1.087309 0.446192 2.014842
TFX 1.064959 1.086960 0.586195 1.671386
CERN 1.034136 1.086544 0.713075 1.334735
PLD 1.050604 1.086169 0.640424 1.510336
EA 1.175793 1.085163 0.874267 1.239342
D 1.024802 1.085106 0.665434 1.419263
ROST 0.897766 1.084625 0.508198 1.628735
PEAK 0.853446 1.084327 0.534996 1.471178
WLTW 1.070002 1.083920 0.704344 1.401532
TAP 0.829259 1.083708 0.623714 1.226852
S&P 500 1.026077 1.083678 0.663281 1.427519
SRE 0.906559 1.083412 0.584277 1.432134
UNH 1.116461 1.081663 0.644954 1.600380
ABBV 1.076261 1.080619 0.684495 1.455039
CB 0.931219 1.080034 0.577383 1.493312
FB 1.161341 1.079865 0.690186 1.558204
DG 1.209118 1.079323 0.843963 1.327375
REGN 1.613644 1.078844 1.141529 1.310274
O 0.819199 1.078615 0.529117 1.435395
CTXS 1.255073 1.078470 1.016977 1.144326
HUM 1.132919 1.078083 0.577994 1.818122
DOV 0.974461 1.077884 0.549235 1.646015
EBAY 1.396054 1.077781 0.698859 1.853455
GS 1.009169 1.077753 0.579942 1.614581
LNC 0.834569 1.077507 0.284286 2.724496
KR 1.175109 1.077424 1.043653 1.045045
RE 0.845421 1.076374 0.597229 1.315131
IVZ 0.621122 1.075776 0.422074 1.367937
LVS 0.844951 1.075770 0.644276 1.219102
FTV 0.988797 1.075340 0.547076 1.680790
VTR 0.745649 1.075306 0.368590 1.881304
HLT 0.837013 1.075062 0.565446 1.376914
FE 0.897904 1.074704 0.640857 1.303706
MET 0.837238 1.074394 0.450077 1.731407
CINF 0.657867 1.072406 0.595612 1.029949
ADP 0.949932 1.072373 0.603363 1.468140
ACN 1.044644 1.071751 0.671763 1.450971
FDX 0.924222 1.071749 0.677703 1.272459
UNP 1.077977 1.069637 0.621810 1.620747
DTE 0.917053 1.069376 0.573423 1.495508
UDR 0.878265 1.068450 0.592155 1.388149
EL 1.036893 1.067974 0.683229 1.421042
ZBH 0.926063 1.067534 0.510196 1.700285
CSCO 1.089770 1.067441 0.738527 1.382370
FRC 1.089334 1.067386 0.642366 1.588757
NVR 0.922746 1.067247 0.538664 1.605090
PAYX 0.946665 1.067053 0.558462 1.588609
TJX 0.956246 1.066633 0.575544 1.557671
HSY 0.901971 1.066263 0.695959 1.215472
ALLE 0.947122 1.066180 0.605314 1.467557
PM 0.924604 1.065043 0.685956 1.265589
TMO 1.122164 1.064694 0.759686 1.387387
BXP 0.739420 1.064638 0.530425 1.309380
JNJ 1.056823 1.064500 0.749073 1.325355
AVY 0.959465 1.064110 0.575784 1.565968
ISRG 1.017911 1.061752 0.594786 1.611856
DGX 1.101599 1.061524 0.665899 1.558424
FISV 0.954351 1.061416 0.631976 1.422727
PFG 0.891098 1.060227 0.431583 1.947434
NOC 0.996766 1.058390 0.742904 1.267694
CMCSA 0.967364 1.057688 0.742503 1.231782
TXT 0.868561 1.057504 0.454755 1.806094
PEP 0.964603 1.057247 0.724580 1.259175
SWK 0.956681 1.057216 0.444492 2.035818
Hess 0.931558 1.057171 0.446693 1.972674
KMX 0.995021 1.056944 0.468106 2.011111
AVB 0.802016 1.054941 0.533040 1.426247
FOX 0.873268 1.054850 0.542146 1.527007
BF.B 0.993809 1.054688 0.649616 1.450516
IRM 0.912750 1.054685 0.652490 1.326339
EOG 0.823178 1.054684 0.454534 1.717140
MAR 0.775241 1.054431 0.477595 1.539427
ARE 0.990754 1.054039 0.681128 1.380003
C 0.792941 1.053752 0.452442 1.663182
HCA 0.821848 1.053339 0.531767 1.467242
ROK 1.166043 1.052818 0.587794 1.884239
NWL 0.867266 1.051824 0.530417 1.554502
BAC 0.847964 1.051282 0.518795 1.554757
WELL 0.735259 1.050609 0.494092 1.416417
WHR 0.963461 1.049564 0.430775 2.130956
KEY 0.762703 1.048717 0.427572 1.700935
ESS 0.842950 1.047137 0.558799 1.440597
VAR 0.955464 1.047135 0.619080 1.473889
YUM 0.972984 1.047077 0.544142 1.707714
BRK.B 0.918773 1.044734 0.710567 1.237649
STT 0.948102 1.044522 0.554757 1.636195
AIV 0.799280 1.044499 0.458484 1.669039
DLTR 1.040780 1.044365 0.819245 1.216446
PSA 0.969101 1.044283 0.722232 1.284914
GD 0.910939 1.044194 0.573272 1.521764
KLAC 1.201048 1.043929 0.695517 1.654177
HON 0.931644 1.043242 0.574765 1.553726
COF 0.796171 1.043224 0.414043 1.843247
FOXA 0.869014 1.043099 0.545504 1.527228
WY 0.791216 1.042905 0.436346 1.738679
A 1.116768 1.042135 0.751364 1.426227
DAL 0.609347 1.041384 0.380610 1.537354
AAL 0.678963 1.041271 0.359523 1.813659
CNP 0.738269 1.040453 0.470451 1.508264
SJM 1.011010 1.040369 0.866407 1.121620
AME 1.002999 1.039814 0.578916 1.666209
JPM 0.840487 1.038915 0.574805 1.407440
UNM 0.640345 1.038621 0.344622 1.789017
ABT 1.054942 1.038507 0.710151 1.430436
National 0.660659 1.038242 0.412786 1.541538
TFC 0.831176 1.037516 0.464473 1.724796
SBUX 0.961827 1.037471 0.638262 1.452520
RTX 0.792411 1.037227 0.500000 1.527942
GRMN 1.002909 1.036987 0.637511 1.517052
FLT 0.922341 1.036511 0.541038 1.644712
URI 1.071946 1.034478 0.455240 2.276203
TSCO 1.275909 1.034267 0.788485 1.564566
KMB 1.013019 1.034039 0.790181 1.239808
MTB 0.751829 1.033720 0.519672 1.399546
PRU 0.762781 1.033275 0.418213 1.765171
MTD 1.174781 1.032433 0.780272 1.458306
EXPD 1.065354 1.031857 0.740162 1.394916
WAB 0.924163 1.030858 0.521690 1.718454
ADM 0.990450 1.030282 0.662672 1.450699
WRK 0.813865 1.029710 0.611837 1.291818
RHI 1.004337 1.028105 0.576612 1.694176
TEL 1.005967 1.026740 0.588356 1.665264
BKNG 0.943332 1.025698 0.584623 1.573145
EQR 0.788131 1.025519 0.594792 1.292080
PPL 0.869213 1.024595 0.524993 1.615923
DRI 0.725646 1.023145 0.339990 2.086035
ANTM 1.001775 1.022827 0.594271 1.648100
Halliburton 0.676293 1.022357 0.238724 2.770993
SHW 1.024470 1.022352 0.679188 1.475397
CBOE 0.813706 1.021540 0.623138 1.278286
CDW 0.926180 1.021503 0.619213 1.464252
LUV 0.684475 1.020796 0.592378 1.131930
PNC 0.845920 1.020680 0.522009 1.587674
CME 0.938275 1.020607 0.667655 1.376953
ICE 1.048114 1.020253 0.702132 1.463126
ORCL 0.992097 1.020217 0.796073 1.221543
MGM 0.683664 1.017941 0.282931 2.373771
MDT 0.911560 1.017152 0.640436 1.399342
CAG 1.137121 1.016672 0.862115 1.297360
PG 0.950213 1.016462 0.771844 1.211157
GIS 1.144465 1.015572 0.873453 1.290186
PNR 0.952229 1.013421 0.512907 1.831947
T 0.860105 1.013386 0.693344 1.224131
HRB 0.811030 1.012399 0.522804 1.532310
CFG 0.762513 1.010949 0.409232 1.843100
HOLX 1.005178 1.010740 0.557495 1.783867
PKI 1.130115 1.009959 0.727224 1.538688
GLW 0.952177 1.009636 0.627430 1.503099
AMCR 1.068762 1.008841 0.644596 1.643505
EXR 0.970784 1.008079 0.695275 1.385069
APH 1.038936 1.006991 0.665317 1.550725
HRL 1.060773 1.006188 0.929277 1.134484
MOS 0.767556 1.005444 0.499188 1.529284
FITB 0.791709 1.005393 0.407643 1.931743
SNA 0.956210 1.004900 0.597499 1.592550
PEG 0.907517 1.004242 0.622846 1.450895
MarathonPet 0.692321 1.003831 0.275805 2.500602
GILD 1.149813 1.003745 1.083731 1.057017
PBCT 0.825660 1.003683 0.654434 1.257009
TIF 0.912279 1.003515 0.906699 1.002630
DE 1.008774 1.003388 0.670265 1.499955
JCI 0.855685 1.002611 0.556345 1.534043
OMC 0.772889 1.001780 0.613100 1.258385
FLS 0.682437 1.001296 0.415102 1.641892
JNPR 1.023055 1.001235 0.710526 1.438079
HBI 0.877717 1.000000 0.610054 1.438753
K 0.998326 0.999087 0.815593 1.225168
AVGO 1.023763 0.998450 0.625631 1.638910
XRAY 0.871332 0.997395 0.555362 1.573041
HOG 0.706700 0.997220 0.427655 1.657106
BR 1.040325 0.996879 0.694184 1.503323
HSIC 0.951988 0.996674 0.626524 1.524548
Williams 0.955264 0.996385 0.505669 1.895964
MAS 1.067149 0.996270 0.606388 1.766437
VNO 0.677924 0.995817 0.476198 1.429598
XLNX 1.066874 0.995786 0.801871 1.336111
MHK 0.805830 0.995105 0.472013 1.715617
HAS 0.812990 0.994168 0.524575 1.558897
UHS 0.774075 0.992980 0.513033 1.519488
REG 0.786743 0.992102 0.544490 1.456422
RF 0.799161 0.991607 0.466747 1.726684
WMT 1.024181 0.991575 0.971026 1.063703
TRV 0.920027 0.991196 0.659501 1.407424
SCHW 0.873343 0.990822 0.586661 1.502456
URI 0.917414 0.990284 0.700000 1.323450
Devon 0.652077 0.989281 0.279007 2.362460
BSX 0.902813 0.988142 0.607765 1.503291
EMN 1.025327 0.988137 0.517874 2.003647
ADS 0.578523 0.988111 0.300990 1.945197
NRG 0.929150 0.987854 0.567623 1.657040
COTY 0.438687 0.986183 0.450963 0.986408
APTV 0.929414 0.986143 0.510484 1.846237
GM 0.855028 0.985754 0.498725 1.739205
BDX 0.940831 0.985344 0.791367 1.206551
CPB 0.994017 0.984939 0.899246 1.122292
AFL 0.774508 0.983948 0.498932 1.577657
JBHT 1.080061 0.983630 0.718807 1.527581
IPGP 1.183037 0.983386 0.781004 1.540356
IT 0.855893 0.982375 0.548693 1.587862
ALGN 1.037809 0.981203 0.505413 2.092724
EMR 0.912925 0.979864 0.528742 1.762080
PWR 1.053429 0.979622 0.603754 1.781092
CHRW 1.087315 0.979448 0.845229 1.313409
GWW 1.018809 0.979234 0.675143 1.541028
HST 0.798291 0.978917 0.621653 1.311798
CAT 0.964316 0.977234 0.670781 1.471094
ALK 0.638374 0.976689 0.367656 1.777778
QRVO 1.111891 0.976257 0.682946 1.667676
FRT 0.763345 0.974800 0.522287 1.499325
AIG 0.711407 0.974369 0.387550 1.883938
Diamond 0.648088 0.974181 0.259557 2.563083
LKQ 0.864896 0.971722 0.408583 2.178417
Conoco 0.801418 0.971136 0.416950 1.979226
BEN 0.857575 0.969777 0.596026 1.483660
CCL 0.490201 0.968551 0.282353 1.792500
KSS 0.574703 0.968000 0.292383 2.030558
WAT 0.928784 0.966170 0.747032 1.286832
NTRS 0.852925 0.965770 0.614508 1.437173
BA 0.588895 0.963995 0.314084 1.944991
VMC 0.877802 0.960257 0.604021 1.513411
AOS 1.121415 0.960104 0.799641 1.460674
HWM 0.565963 0.960092 NaN 1.300000
GPC 0.889274 0.959911 0.504869 1.834957
VZ 0.953907 0.959689 0.866070 1.147684
CVS 0.911333 0.959200 0.737976 1.287436
XRX 0.481356 0.959061 0.438010 1.145872
ZBRA 1.096126 0.958572 0.703362 1.625761
MLM 0.836382 0.957519 0.558432 1.564183
MKC 0.996849 0.956002 0.694806 1.500747
DISCK 0.746742 0.955563 0.575524 1.357837
HIG 0.767599 0.955263 0.528753 1.519700
KEYS 1.031504 0.954440 0.824396 1.310952
VFC 0.775897 0.953998 0.547565 1.485323
Cabot 1.268323 0.953416 0.921173 1.444130
HBAN 0.740067 0.952189 0.495049 1.570000
EW 0.906251 0.950120 0.672182 1.419004
AMGN 0.964897 0.949803 0.836995 1.213736
TGT 0.975988 0.949727 0.817605 1.256903
MMM 1.001615 0.948666 0.743378 1.420294
IFF 0.984574 0.947650 0.710528 1.462240
DIS 0.841786 0.946655 0.610957 1.455457
PCAR 0.976328 0.946142 0.660710 1.561811
TTWO 1.056986 0.943998 0.953878 1.173830
SPG 0.594073 0.942269 0.356934 1.766348
ZION 0.786194 0.941763 0.520649 1.603405
PFE 0.943629 0.939958 0.794700 1.263250
ILMN 1.107701 0.939877 0.791320 1.489359
BK 0.867159 0.939524 0.599825 1.538741
Chevron 0.862066 0.939029 0.493762 1.859277
CMI 1.011065 0.938665 0.614888 1.751752
CTL 0.758065 0.937588 0.649963 1.243959
SYY 0.751189 0.935602 0.472820 1.698098
AAP 0.959102 0.935338 0.550955 1.861147
Schlumberger 0.607626 0.934789 0.379249 1.713952
IP 0.849217 0.934508 0.616298 1.474499
STX 0.927524 0.933663 0.744840 1.333742
HII 0.795953 0.932888 0.648513 1.315646
DOW 0.841048 0.932670 0.545119 1.654251
Baker 0.800631 0.931890 0.451597 1.902465
DISCA 0.769565 0.931056 0.616744 1.340184
AZO 0.992125 0.930728 0.668089 1.595545
MRK 0.927710 0.930303 0.804946 1.238855
SEE 0.934806 0.929750 0.633371 1.587438
FFIV 1.010865 0.925268 0.742567 1.471262
PPG 0.882649 0.924555 0.607406 1.571724
USB 0.714382 0.923959 0.527920 1.464570
HPE 0.705845 0.919314 0.541120 1.418902
RCL 0.578185 0.914238 0.256740 2.463285
UPS 0.915686 0.914227 0.863073 1.160501
LYB 0.818430 0.911128 0.485783 1.849099
MO 0.836360 0.909200 0.688762 1.335564
UAL 0.481003 0.906317 0.328495 1.615619
DFS 0.724860 0.906254 0.351629 2.274672
NTAP 0.809909 0.905123 0.658164 1.359547
NCLH 0.418392 0.904868 0.199340 2.319545
CMA 0.649173 0.904592 0.431026 1.664960
Valero 0.800772 0.903015 0.387549 2.288167
Technip 0.500783 0.900261 0.365429 1.522222
SYF 0.680896 0.897545 0.380499 1.993750
F 0.820112 0.897207 0.499377 1.830424
ORLY 0.956140 0.893890 0.660256 1.620037
PVH 0.644581 0.892450 0.334369 2.160069
CE 0.800262 0.891819 0.571166 1.571061
KIM 0.715562 0.891396 0.496308 1.617428
PKG 0.994423 0.891347 0.769122 1.450538
WFC 0.594853 0.889181 0.533376 1.254257
ALXN 0.997921 0.888177 0.791203 1.420067
Noble 0.538498 0.887135 0.240970 2.519016
UAA 0.596454 0.886620 0.488485 1.377171
KHC 1.051241 0.883661 0.757476 1.570534
TSN 0.727476 0.883012 0.730964 1.127083
CF 0.730847 0.881840 0.558257 1.484573
WBA 0.769818 0.880496 0.840756 1.039899
UA 0.578483 0.878307 0.477912 1.378151
MKTX 1.253585 0.874642 0.816338 1.755714
Exxon 0.773197 0.871959 0.525393 1.687758
LEG 0.716766 0.871889 0.497234 1.653316
INCY 0.984316 0.871263 0.763320 1.480057
ABMD 1.288411 0.866883 0.787363 1.887638
NUE 0.814437 0.857066 0.593939 1.599930
MarathonOil 0.601479 0.842235 0.326829 2.185075
Holly 0.724104 0.839110 0.437925 1.970525
BWA 0.889127 0.838102 0.601707 1.763121
DD 0.894376 0.837183 0.541579 1.972593
DXC 0.494913 0.835854 0.356579 1.660517
NLOK 0.829393 0.819149 0.797499 1.269602
Phillips66 0.776406 0.801190 0.466681 2.076503
VIAC 0.590308 0.716838 0.385114 2.138298
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